{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# world height\n",
    "WORLD_HEIGHT = 4\n",
    "\n",
    "# world width\n",
    "WORLD_WIDTH = 12\n",
    "\n",
    "# probability for exploration\n",
    "EPSILON = 0.1\n",
    "\n",
    "# step size\n",
    "ALPHA = 0.5\n",
    "\n",
    "# gamma for Q-Learning and Expected Sarsa\n",
    "GAMMA = 1\n",
    "\n",
    "# all possible actions\n",
    "ACTION_UP = 0\n",
    "ACTION_DOWN = 1\n",
    "ACTION_LEFT = 2\n",
    "ACTION_RIGHT = 3\n",
    "ACTIONS = [ACTION_UP, ACTION_DOWN, ACTION_LEFT, ACTION_RIGHT]\n",
    "\n",
    "# initial state action pair values\n",
    "START = [3, 0]\n",
    "GOAL = [3, 11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def step(state, action):\n",
    "    i, j = state\n",
    "    if action == ACTION_UP:\n",
    "        next_state = [max(i - 1, 0), j]\n",
    "    elif action == ACTION_LEFT:\n",
    "        next_state = [i, max(j - 1, 0)]\n",
    "    elif action == ACTION_RIGHT:\n",
    "        next_state = [i, min(j + 1, WORLD_WIDTH - 1)]\n",
    "    elif action == ACTION_DOWN:\n",
    "        next_state = [min(i + 1, WORLD_HEIGHT - 1), j]\n",
    "    else:\n",
    "        assert False\n",
    "\n",
    "    reward = -1\n",
    "    if (action == ACTION_DOWN and i == 2 and 1 <= j <= 10) or (\n",
    "        action == ACTION_RIGHT and state == START):\n",
    "        reward = -100\n",
    "        next_state = START\n",
    "\n",
    "    return next_state, reward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose an action based on epsilon greedy algorithm\n",
    "def choose_action(state, q_value):\n",
    "    if np.random.binomial(1, EPSILON) == 1:\n",
    "        return np.random.choice(ACTIONS)\n",
    "    else:\n",
    "        values_ = q_value[state[0], state[1], :]\n",
    "        return np.random.choice([action_ for action_, value_ in enumerate(values_) if value_ == np.max(values_)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# an episode with Sarsa\n",
    "# @q_value: values for state action pair, will be updated\n",
    "# @expected: if True, will use expected Sarsa algorithm\n",
    "# @step_size: step size for updating\n",
    "# @return: total rewards within this episode\n",
    "def sarsa(q_value, expected=False, step_size=ALPHA):\n",
    "    state = START\n",
    "    action = choose_action(state, q_value)\n",
    "    rewards = 0.0\n",
    "    while state != GOAL:\n",
    "        next_state, reward = step(state, action)\n",
    "        next_action = choose_action(next_state, q_value)\n",
    "        rewards += reward\n",
    "        if not expected:\n",
    "            target = q_value[next_state[0], next_state[1], next_action]\n",
    "        else:\n",
    "            target = 0.0\n",
    "            q_next = q_value[next_state[0], next_state[1], :]\n",
    "            best_actions = np.argwhere(q_next == np.max(q_next))\n",
    "            for action_ in ACTIONS:\n",
    "                if action_ in best_actions:\n",
    "                    target += ((1.0 - EPSILON) / len(best_actions) + EPSILON / len(ACTIONS)) * q_value[next_state[0], next_state[1], action_]\n",
    "                else:\n",
    "                    target += EPSILON / len(ACTIONS) * q_value[next_state[0], next_state[1], action_]\n",
    "\n",
    "        target *= GAMMA\n",
    "        q_value[state[0], state[1], action] += step_size * (\n",
    "                reward + target - q_value[state[0], state[1], action])\n",
    "        state = next_state\n",
    "        action = next_action\n",
    "    return rewards\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def q_learning(q_value, step_size=ALPHA):\n",
    "    state = START\n",
    "    rewards = 0.0\n",
    "    while state != GOAL:\n",
    "        action = choose_action(state, q_value)\n",
    "        next_state, reward = step(state, action)\n",
    "        rewards += reward\n",
    "        # Q-Learning update\n",
    "        q_value[state[0], state[1], action] += step_size * (\n",
    "                reward + GAMMA * np.max(q_value[next_state[0], next_state[1], :]) -\n",
    "                q_value[state[0], state[1], action])\n",
    "        state = next_state\n",
    "    return rewards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# print optimal policy\n",
    "def print_optimal_policy(q_value):\n",
    "    optimal_policy = []\n",
    "    for i in range(0, WORLD_HEIGHT):\n",
    "        optimal_policy.append([])\n",
    "        for j in range(0, WORLD_WIDTH):\n",
    "            if [i, j] == GOAL:\n",
    "                optimal_policy[-1].append('G')\n",
    "                continue\n",
    "            bestAction = np.argmax(q_value[i, j, :])\n",
    "            if bestAction == ACTION_UP:\n",
    "                optimal_policy[-1].append('U')\n",
    "            elif bestAction == ACTION_DOWN:\n",
    "                optimal_policy[-1].append('D')\n",
    "            elif bestAction == ACTION_LEFT:\n",
    "                optimal_policy[-1].append('L')\n",
    "            elif bestAction == ACTION_RIGHT:\n",
    "                optimal_policy[-1].append('R')\n",
    "    for row in optimal_policy:\n",
    "        print(row)\n",
    "    \n",
    "    optimal_policy = []\n",
    "    for i in range(0, WORLD_HEIGHT):\n",
    "        optimal_policy.append([])\n",
    "        for j in range(0, WORLD_WIDTH):\n",
    "            if [i, j] == GOAL:\n",
    "                optimal_policy[-1].append('G')\n",
    "                continue\n",
    "            if [i, j] == START:\n",
    "                optimal_policy[-1].append('S')\n",
    "                continue\n",
    "            optimal_policy[-1].append('.')\n",
    "                \n",
    "    \n",
    "    state = START\n",
    "    STEP_NUM = 0\n",
    "    while state != GOAL:\n",
    "        i, j = state\n",
    "        bestAction = np.argmax(q_value[i, j, :])\n",
    "        next_state, _ = step(state, bestAction)\n",
    "        if bestAction == ACTION_UP:\n",
    "            optimal_policy[i][j] = 'U'\n",
    "        elif bestAction == ACTION_DOWN:\n",
    "            optimal_policy[i][j] = 'D'\n",
    "        elif bestAction == ACTION_LEFT:\n",
    "            optimal_policy[i][j] = 'L'\n",
    "        elif bestAction == ACTION_RIGHT:\n",
    "            optimal_policy[i][j] = 'R'\n",
    "        state = next_state\n",
    "        STEP_NUM += 1\n",
    "    print('Path is:', STEP_NUM)\n",
    "    for row in optimal_policy:\n",
    "        print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Use multiple runs instead of a single run and a sliding window\n",
    "# With a single run I failed to present a smooth curve\n",
    "# However the optimal policy converges well with a single run\n",
    "# Sarsa converges to the safe path, while Q-Learning converges to the optimal path\n",
    "def figure_6_4():\n",
    "    # episodes of each run\n",
    "    episodes = 2000\n",
    "\n",
    "    # perform 40 independent runs\n",
    "    runs = 50\n",
    "\n",
    "    rewards_sarsa = np.zeros(episodes)\n",
    "    rewards_q_learning = np.zeros(episodes)\n",
    "    for r in tqdm(range(runs)):\n",
    "        q_sarsa = np.zeros((WORLD_HEIGHT, WORLD_WIDTH, 4))\n",
    "        q_q_learning = np.copy(q_sarsa)\n",
    "        for i in range(0, episodes):\n",
    "            # cut off the value by -100 to draw the figure more elegantly\n",
    "            # rewards_sarsa[i] += max(sarsa(q_sarsa), -100)\n",
    "            # rewards_q_learning[i] += max(q_learning(q_q_learning), -100)\n",
    "            rewards_sarsa[i] += sarsa(q_sarsa)\n",
    "            rewards_q_learning[i] += q_learning(q_q_learning)\n",
    "\n",
    "    # averaging over independt runs\n",
    "    rewards_sarsa /= runs\n",
    "    rewards_q_learning /= runs\n",
    "\n",
    "    # draw reward curves\n",
    "    plt.plot(rewards_sarsa, label='Sarsa')\n",
    "    plt.plot(rewards_q_learning, label='Q-Learning')\n",
    "    plt.xlabel('Episodes')\n",
    "    plt.ylabel('Sum of rewards during episode')\n",
    "    plt.ylim([-100, 0])\n",
    "    plt.legend()\n",
    "\n",
    "    # plt.savefig('../images/figure_6_4.png')\n",
    "    # plt.close()\n",
    "\n",
    "    # display optimal policy\n",
    "    print('Sarsa Optimal Policy:')\n",
    "    print_optimal_policy(q_sarsa)\n",
    "    print('Q-Learning Optimal Policy:')\n",
    "    print_optimal_policy(q_q_learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [02:29<00:00,  2.99s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sarsa Optimal Policy:\n",
      "['R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'D', 'R', 'R', 'D']\n",
      "['U', 'U', 'U', 'R', 'U', 'U', 'R', 'R', 'R', 'R', 'R', 'D']\n",
      "['U', 'U', 'U', 'L', 'U', 'U', 'U', 'U', 'U', 'U', 'R', 'D']\n",
      "['U', 'U', 'U', 'U', 'U', 'U', 'U', 'U', 'U', 'U', 'U', 'G']\n",
      "Path is: 17\n",
      "['R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'D', '.', '.', '.']\n",
      "['U', '.', '.', '.', '.', '.', '.', '.', 'R', 'R', 'R', 'D']\n",
      "['U', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', 'D']\n",
      "['U', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', 'G']\n",
      "Q-Learning Optimal Policy:\n",
      "['R', 'R', 'R', 'R', 'R', 'R', 'R', 'D', 'R', 'R', 'D', 'D']\n",
      "['D', 'D', 'R', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D']\n",
      "['R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'D']\n",
      "['U', 'U', 'U', 'U', 'U', 'U', 'U', 'U', 'U', 'U', 'U', 'G']\n",
      "Path is: 13\n",
      "['.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.']\n",
      "['.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.']\n",
      "['R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'D']\n",
      "['U', '.', '.', '.', '.', '.', '.', '.', '.', '.', '.', 'G']\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAG2CAYAAAB1ZSLWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC1f0lEQVR4nOydd3wUxfvHP3uXTkhCCyGQUALSey8CIh2kqaAgTUQRVAS+dBXUn4CgYEFERQQREWyoiEDovRM6SAchCSUkoaXe/v643GV3b+vd7pXkeb9eaG53dubZ3dmZZ5555hmGZVkWBEEQBEEQBADA5GkBCIIgCIIgvAlSjgiCIAiCIDiQckQQBEEQBMGBlCOCIAiCIAgOpBwRBEEQBEFwIOWIIAiCIAiCAylHBEEQBEEQHEg5IgiCIAiC4EDKEUEQBEEQBAdSjgiCIAiCIDgUWuXoiy++QIUKFRAUFISmTZti//79nhaJIAiCIAgvoFAqRytXrsTYsWMxbdo0HD58GHXr1kWnTp1w8+ZNT4tGEARBEISHYQrjxrNNmzZF48aNMX/+fACAxWJBTEwMXn/9dUyaNMnD0hEEQRAE4Un8PC2Au8nKysKhQ4cwefJk+zGTyYT27dtjz549DukzMzORmZlp/22xWJCSkoISJUqAYRi3yEwQBEEQhGuwLIt79+4hOjoaJpP8xFmhU45u376N3NxclC5dmne8dOnSOHPmjEP6mTNn4t1333WXeARBEARBGMi1a9dQrlw52TSFTjnSyuTJkzF27Fj777S0NMTGxuLatWsICwvzoGQEQRAEQaglPT0dMTExKFq0qGLaQqcclSxZEmazGcnJybzjycnJiIqKckgfGBiIwMBAh+NhYWGkHBEEQRCEj6HGJabQrVYLCAhAw4YNsWnTJvsxi8WCTZs2oXnz5h6UjCAIgiAIb6DQWY4AYOzYsRg8eDAaNWqEJk2a4JNPPsGDBw8wdOhQT4tGEARBEISHKZTKUb9+/XDr1i288847SEpKQr169bBu3ToHJ22CIAiCIAofhTLOkSukp6cjPDwcaWlp5HNEEAThA+Tm5iI7O9vTYhBuICAgQHKZvpb+u1BajgiCIIiCD8uySEpKQmpqqqdFIdyEyWRCxYoVERAQ4FI+pBwRBEEQBRKbYhQZGYmQkBAK3FvAsVgsuHHjBhITExEbG+vS+ybliCAIgihw5Obm2hWjEiVKeFocwk2UKlUKN27cQE5ODvz9/Z3Op9At5ScIgiAKPjYfo5CQEA9LQrgT23Rabm6uS/mQckQQBEEUWGgqrXCh1/sm5YggCIIgCIIDKUcEQRAEQRAcSDkiCIIgCC/i1q1bePXVVxEbG4vAwEBERUWhU6dO2LVrl6dFKzTQajWCIAiC8CKefvppZGVlYenSpahUqRKSk5OxadMm3Llzx6n8cnNzwTCMZHBEwhF6UgRBEAThJaSmpmLHjh348MMP8cQTT6B8+fJo0qQJJk+ejB49egAA5s6di9q1a6NIkSKIiYnByJEjcf/+fXseS5YsQUREBP7880/UqFEDgYGBuHr1KrZu3YomTZqgSJEiiIiIQMuWLXHlyhUAwIULF9CzZ0+ULl0aoaGhaNy4MTZu3OiRZ+ANkOWIIAiCKPCwLItH2a4t73aWYH+z6lVUoaGhCA0NxerVq9GsWTMEBgY6pDGZTPjss89QsWJFXLx4ESNHjsSECROwYMECe5qHDx/iww8/xKJFi1CiRAkUL14c9erVw/Dhw7FixQpkZWVh//79drnu37+Prl274oMPPkBgYCC+//57PPXUUzh79ixiY2P1eRA+BO2tphHaW40gCML7ycjIwKVLl1CxYkUEBQXhYVYOaryz3iOynHqvE0IC1Nsifv31VwwfPhyPHj1CgwYN0KZNGzz33HOoU6eOaPpffvkFI0aMwO3btwFYLUdDhw5FQkIC6tatCwBISUlBiRIlsHXrVrRp00aVHLVq1cKIESPw2muvqZbd0wjfOxct/TdNqxEEQRCEF/H000/jxo0b+PPPP9G5c2ds3boVDRo0wJIlSwAAGzduxJNPPomyZcuiaNGiGDhwIO7cuYOHDx/a8wgICOApU8WLF8eQIUPQqVMnPPXUU/j000+RmJhoP3///n3873//Q/Xq1REREYHQ0FCcPn0aV69eddt9exNkOdIIWY4IgiC8H6EFwVem1aR46aWXEB8fj23btqFatWp49dVX0a9fPxQvXhw7d+7EsGHDcPfuXURERGDJkiV48803RTfcPXLkCNatW4e//voLx48fR3x8PJo1a4YRI0YgPj4eH330ESpXrozg4GA888wzaNu2LT755BOXZHcnelmOyOeIIAiCKPAwDKNpasvbqFGjBlavXo1Dhw7BYrHg448/tq8+W7Vqlep86tevj/r162Py5Mlo3rw5fvzxRzRr1gy7du3CkCFD0Lt3bwBWS9Lly5eNuBWfgKbVCIIgCMJLuHPnDtq1a4cffvgBx44dw6VLl/Dzzz9j9uzZ6NmzJypXrozs7Gx8/vnnuHjxIpYtW4aFCxcq5nvp0iVMnjwZe/bswZUrV7BhwwacO3cO1atXBwBUqVIFv/32GxISEnD06FH0798fFovF6Nv1WnxXjSYIgiCIAkZoaCiaNm2KefPm4cKFC8jOzkZMTAyGDx+OKVOmIDg4GHPnzsWHH36IyZMno3Xr1pg5cyYGDRokm29ISAjOnDmDpUuX4s6dOyhTpgxGjRqFV155BYA1PMCLL76IFi1aoGTJkpg4cSLS09PdccteCfkcaYR8jgiCILwfOd8TouBCq9UIgiAIgiAMgJQjgiAIgiAIDqQcEQRBEARBcCDliCAIgiAIggMpRwRBEARBEBxIOSIIgiAIguBAyhFBEARBEAQHUo4IgiAIgiA4kHJEEARBEATBgZQjgiAIgiAMp23btnjzzTc9LYYqSDkiCIIgCC/j2rVrePHFFxEdHY2AgACUL18eo0ePxp07d2Svmz59OurVq+ceITXy22+/4f333/e0GKog5YggCIIgvIiLFy+iUaNGOHfuHFasWIHz589j4cKF2LRpE5o3b46UlBRPi8gjOztbVbrixYujaNGiBkujD6QcEQRBEIQXMWrUKAQEBGDDhg1o06YNYmNj0aVLF2zcuBHXr1/H1KlTnc772rVr6Nu3LyIiIlC8eHH07NkTly9ftp8/cOAAOnTogJIlSyI8PBxt2rTB4cOHeXkwDIMvv/wSPXr0QJEiRfDBBx/YLVbLli1DhQoVEB4ejueeew737t2zXyecVqtQoQJmzJiBF198EUWLFkVsbCy+/vprXlm7d+9GvXr1EBQUhEaNGmH16tVgGAYJCQlOPwM1kHJEEARBFHxYFsh64Jl/LKtazJSUFKxfvx4jR45EcHAw71xUVBQGDBiAlStXgtWQp43s7Gx06tQJRYsWxY4dO7Br1y6Ehoaic+fOyMrKAgDcu3cPgwcPxs6dO7F3715UqVIFXbt25Sk5gHX6rnfv3jh+/DhefPFFAMCFCxewevVqrFmzBmvWrMG2bdswa9YsWZk+/vhjNGrUCEeOHMHIkSPx6quv4uzZswCA9PR0PPXUU6hduzYOHz6M999/HxMnTtR8387g55ZSCIIgCMKTZD8EZkR7puwpN4CAIqqSnjt3DizLonr16qLnq1evjrt37+LWrVuIjIzUJMbKlSthsViwaNEiMAwDAPjuu+8QERGBrVu3omPHjmjXrh3vmq+//hoRERHYtm0bunfvbj/ev39/DB06lJfWYrFgyZIl9qmzgQMHYtOmTfjggw8kZeratStGjhwJAJg4cSLmzZuHLVu2oGrVqvjxxx/BMAy++eYbBAUFoUaNGrh+/TqGDx+u6b6dgSxHBEEQBOFlKFmGMjIyEBoaav83Y8YMxTyPHj2K8+fPo2jRovbrihcvjoyMDFy4cAEAkJycjOHDh6NKlSoIDw9HWFgY7t+/j6tXr/LyatSokUP+FSpU4PkUlSlTBjdv3pSVqU6dOva/GYZBVFSU/ZqzZ8+iTp06CAoKsqdp0qSJ4n3qAVmOCIIgiIKPf4jVguOpslVSuXJlMAyD06dPo3fv3g7nT58+jVKlSiE6Oprnd1O8eHHFvO/fv4+GDRti+fLlDudKlSoFABg8eDDu3LmDTz/9FOXLl0dgYCCaN29un3azUaSIoyXM39+f95thGFgsFlmZnLnGHZByRBAEQRR8GEb11JYnKVGiBDp06IAFCxZgzJgxPL+jpKQkLF++HKNGjYKfnx8qV66sKe8GDRpg5cqViIyMRFhYmGiaXbt2YcGCBejatSsAqwP37du3nb8hF6hatSp++OEHZGZmIjAwEIDVYdwd0LQaQRAEQXgR8+fPR2ZmJjp16oTt27fj2rVrWLduHTp06IDHHnsM77zzjuz1jx49QkJCAu/fhQsXMGDAAJQsWRI9e/bEjh07cOnSJWzduhVvvPEG/vvvPwBAlSpVsGzZMpw+fRr79u3DgAEDHBzD3UX//v1hsVjw8ssv4/Tp01i/fj0++ugjALD7TBkFKUcEQRAE4UVUqVIFBw4cQKVKldC3b1+UL18eXbp0wWOPPWZfYSbHv//+i/r16/P+vfLKKwgJCcH27dsRGxuLPn36oHr16hg2bBgyMjLslqRvv/0Wd+/eRYMGDTBw4EC88cYbmh2/9SIsLAx//fUXEhISUK9ePUydOtWuGHL9kIyAYZ1ZD1iISU9PR3h4ONLS0iTNkgRBEIRnycjIwKVLl1CxYkXDO1J3MG3aNMydOxfx8fFo1qyZp8XxGMuXL8fQoUORlpYmatGSe+9a+m/yOSIIgiAIL+fdd99FhQoVsHfvXjRp0gQmU+GY+Pn+++9RqVIllC1bFkePHsXEiRPRt29fw6f6SDkiCIIgCB9AGFeoMJCUlIR33nkHSUlJKFOmDJ599lnZuEl6QcoRQRAEQRBeyYQJEzBhwgS3l1s47HIEQRAEQRAqIeWIIAiCKLDQmqPChV7vm5QjgiAIosBhi7z88OFDD0tCuBNbJG+z2exSPuRzRBAEQRQ4zGYzIiIi7Pt0hYSEGB44kPAsFosFt27dQkhICPz8XFNvSDkiCIIgCiRRUVEAoLj5KVFwMJlMiI2NdVkRJuWIIAiCKJAwDIMyZcogMjIS2dnZnhaHcAMBAQG6xIAi5YggCIIo0JjNZpd9UIjCBTlkEwRBEARBcCDliCAIgiAIggMpRwRBEARBEBxIOSIIgiAIguBAyhFBEARBEAQHUo4IgiAIgiA4kHJEEARBEATBgZQjgiAIgiAIDqQcEQRBEARBcCDliCAIgiAIggMpRwRBEARBEBxIOSIIgiAIguBAyhFBEARBEAQHUo4IgiAIgiA4FBjl6PLlyxg2bBgqVqyI4OBgxMXFYdq0acjKyuKlO3bsGB5//HEEBQUhJiYGs2fP9pDEBEFoZdPpZFy+/cCQvFmWxdmke3iUlWtI/gRB5JOTa/G0CLIUGOXozJkzsFgs+Oqrr3Dy5EnMmzcPCxcuxJQpU+xp0tPT0bFjR5QvXx6HDh3CnDlzMH36dHz99dcelJwgCDmyciy4dPsBdl+4jWFLD6LtR1sNKWfL2Zvo9Ml29F6wS9N1LMsaIg/hXfxzPBFnk+55WowCwfqTSajxznqsOXbD06JI4udpAfSic+fO6Ny5s/13pUqVcPbsWXz55Zf46KOPAADLly9HVlYWFi9ejICAANSsWRMJCQmYO3cuXn75ZU+JTmjgl0P/4Yst5/HNoEaoHBnqaXEINzB0yX7sOn8H1cuEGVrOr4euAwDOaOgAR/90BMf+S8M/ox9HkL9ZMT3LsrCwgNnEOC2nnnyx5Tx2X7iNxUMaI9BPWX6j+ed4IjJyctG7fjlPi8Jj/6UUvLr8MADgyNsdUKxIgEv5rTuRiK1nb+HdnjUdnntWjgV3H2ahdFiQS2V4Kxdv3ccryw4BAF778QjKRgSjfmwxD0vlSIGxHImRlpaG4sWL23/v2bMHrVu3RkBAfsXu1KkTzp49i7t374rmkZmZifT0dN4/wnP87+ejuHT7Aab8dhwJ11Lx8YazyMjmT4OwLOtwTIktZ29i2h8nkJlTOKdUziXfw6Er4t+AnmRk5ypaWh5l5eJ+Zo79967zdwAApxP1//aW7r6M5jM34eKt+05d/0fCDVy6/QBbz97Cn0dvYNneK5Jpk9IyUHHyWsRNWStaz77Ych4VJv2NQ1dSVJV990GW5qmJY/+lYuOpZPvvOevPYtf5O1h/MlnmKnFYlsWWszdxMz0DSWkZeP7rvVh3IklzPjZyci14dflhjFl5FLfuZUqmy8zJlZxa3XfxDjrN2479l9Q9Q7WcTcqve68uP8Q7x7Iszt+87/AubM/nzn3Hexnxw2H8dOAalu6+7HBuwKK9aDpjkyH13RsY9/NR3u/eC3Z7SBJ5CqxydP78eXz++ed45ZVX7MeSkpJQunRpXjrb76Qk8Y965syZCA8Pt/+LiYkxTmhCNRk5uej1xS58vvk8Xl9xBD3m78TuC7cBAAO/3Y8a76zD3QdZCrnkM/S7A1i65wqW7ZHu3DyJmELxxZbz+CPhui75d5i3HU9/uRvJ6RkAgPSMbF3y5XIj9RGqvb0Or/14RDINy7Ko8+561Jq2HhnZucg22C9h2p8nkZiWgel/ndJ0XUZ2Lib+cox37I0VR/D26hO4kfpI9JqJv+anr/rWOqw6eA1JaRn2Y3PWnwUAPP3lHsXyL9y6j/rvx2Pwd/s1yd1j/i689P1BXLh1n1enzIx2S9ZfxxIx9LsDaDV7C6b9eQJ7Lt7BiB+sisPl2w+Q9khbHcqx5MvDVY6FPPPlHrT9aCt2nrvtcK7f13txNvke+n7Ff4bb/r2Fod/tR2Ka+LsRIqz/Jo6lb+9FvuL14/6raD93m0On//dx6/OxWUlscJ/71ZSHDmUfuGwdpPy0/6oqWT3B/cwc5Fqcm05Oe6h/22IEXq8cTZo0CQzDyP47c+YM75rr16+jc+fOePbZZzF8+HCXyp88eTLS0tLs/65du+ZSfoWBxLRH+PngNbdZYeJPJePYf2no/80+AMDO87dhYYENp/IV3jv3MxF/KllxpH0jNUP2vBrkRr3OkGth0WvBbozkjFj3X0rBnPVnMfqnBE153cvIxvqTSZLv5r+7D7Fw2wXUmb4Bvxz6TzE/JSudxcLiwOUUPMzKsTf2fx9PlEyfa2GRnWttdK+nPsLCrRcUZeDyKCsXvRfswrz4fzVdt/3fWzhy1dFyJnV/y/ZcwcqD+W0B93k+zHPoZlkWfyRcx/mb1mk6YUc44ZdjaDZzE05cT9Mk66OsXMz6x9rm2axqXDacTMKx/1LzD2Q/AgTK9fW7j3CPo4AUDdLuYbH17E0A1mmgFM5A5EbqIzzx8Va0mrXZrghoteTKqWrH857XL4f4bbHccxy8eD+2nL2Fqb+fAGB9hvsvpYh28HPj/0Wd6Rsw85/T9gGWnPL4xebzAKxWRC4r8ur7QYFF9v/+Pm3/21ZXdp2/jam/H8fDrPx3olW5FOOPhOvYkveehEz/8yQqTPobu847KplyJKY9Qq1p6zF4sbJi/igrFxtPJSMrR90gx+KkwmUEXq8cjRs3DqdPn5b9V6lSJXv6Gzdu4IknnkCLFi0cHK2joqKQnMw3H9t+R0VFiZYfGBiIsLAw3r/CSGZOLj74+5SqD6nzJzsw/pdjWLBFW8dm49a9TEz/86TLzo9XUx7aG+eeX+zC8O8P4nsFy5ATA2gei3ZcROMPNmLB1vO846447Z64noaj11Kx9ni+snc2Of/ZaGlEX1p6EK8sO4SZa/MHFDaLmw1bx/s/wUhYjKmrT6Da2+swavlhrBVRepbuuYxnF+7BoG/3g5F4uGmPsvHuXydx9FoqcgXPaf0pbdM0vx7+D0eupuLTTec0XQcAN9IcFePXVhxBtbfX4ZpAsUlK56fNzM5v/AP9rM3qxtM3MfqnBLSfux0AYJGoA1JK6A97r2DzmWTcy8jGmz8dweE85a393G2IPyU+DfZv8j28vOwQeszfheupj5CTeAL4IAr483WeFc7fbMK/nO9r1cFrmq10jIQK8/Kyg2BZ4F5mDm7fz8KiHRdR7e11WH9S+l0eupKCL7aclzwvhvBpHrmWqniNzTL62o+H0ferPfh6+0Xe+f/uPsRneXXnq20X0XrOFgB8y5EQfz/xbrRIQL7Cue5EEnbntZ3f7rxkP57yIAvjVh3FgEX7sHzfVczfnP8M7me6Nri8nvoIo39KwNDvDoieX5I3pTdg0T5N+f522Gqt3qmiL+j5xU689P1B/Hk0X3EU+w5u38/E5dsP0PD/4jF/s/Zv1wi8XjkqVaoUqlWrJvvP5kN0/fp1tG3bFg0bNsR3330Hk4l/e82bN8f27duRnZ3fmcTHx6Nq1aooVsz7HMK8iaW7L+ObHZdUfUi2znrbv7dU55+VY7E3zhN+OYoluy+j0yfbXZo2+mLLBczbaP3Q/rtrNadvUOhsXfWTtY0KZ6+zTpGwLItBi/ej39d7eQpSTq4FG04mifojCBHrUlPu54/U/2+N+imhfXm+GKs4Vg+bxQ0ATiVqU0h/3JdvDRqZ57DK5af91nIOXrkLk4hytPv8bbyy7CC+23UZPb/YxRvJM3AweCiSqXKEqsTNvE7072NWhe9HhSmODI7lyCYzz3oD6XvJyrU4jJhP3kjDW6tP4MUlB1F7+gasTriBPnm+Gdclpu3SM7Kx6kD+e205azP2L3vL+uPIMruVAgAC/Bg8szB/6mnNsURR/xdnOHE931cmMyfX/k2MXZkgec3TX+7B5xzFYMQPh7D2eKLsFIzweRYJUHYoP3kjHb8e+g+bzlitKcv38QdLLy7hKxL3MqyWHDnLUYBZvBvlKk0jfjiE/iJt59azt/Dr4Xzl+BLHl8rEWJVGZ6e4uW1LZk4uVh24Zp/ydaVdVQp1kWthcTuv7H+T7zuUJ2YcmvXPGcxYexp3H2bjow3arL5G4fXKkVpsilFsbCw++ugj3Lp1C0lJSTxfov79+yMgIADDhg3DyZMnsXLlSnz66acYO3asByX3Dc7fdM5hVQ1pD7PRevYWPPX5TrAsazebA+BNG23nKFtqO83PNp3Dgcv5PgL+eQ1ZVo4FLyzah0828j9EKeuGs9zLzMH2f29h/6UUnrXh+z1X8PKyQ+j62Q78efSGrC8EV2GzdaJcC8vpJGnHzdv3rVa4fRf50y9Sz+/t1Sfsf0s9iu3/3sLJG+qmgrh5CPuQE9fT0H/RPp4PB1c5epiVK9qQCtNx0WtZfZMZmwT5Csvh/+Z2GELrl/24hMwXb93njawBSFqG5Oi7cA8WcawSAH+KV6lTOywyrShGekY2xqxMwI5zyoMfrrIq9VzEOJN0DyOXH0a7j7fyjnOfkzC3EBXKEcB3CC4uWHVm68y5ZObkws8s3S74cSr2tzsv2X2hpJQmObh1ZMOpZDz95R50/2wnAKufz20Vgykb3Me9YMsFTPj1GJ763JqX2HT8ietpeGPFEVy94+gHlZ8ni693XJQ8D1inMBv930Yc5Vjy/DiNmJjlKOVBlqRl1VM4pRwtW7YMLVu2RHR0NK5csWren3zyCf744w9dhdNCfHw8zp8/j02bNqFcuXIoU6aM/Z+N8PBwbNiwAZcuXULDhg0xbtw4vPPOO7SMXwU5udorrtortp+7haT0DJxJumcdRQsu/C1vZDVIxRy3GM9yRsi2j3TdySTsPH8bn2zUx4S78sBV9P9mr8Nx/nRLfuNt6/yS0zPxxoojePLjbZJ5c6cv7B0MpyHhjtSFjFmZgCW7L6Pf13uxZBe/4zz+X5roVFh+uY5cvv0AgxbvR7e8BlsLtlG4DTEfEW7n0HvBLkllx2hHbSGsQm2e+U/+NKXNKueoUInnsfdiCt4UWFW2nFVvdQWsio9SCIJHHL+frBxHWdQODD6JP4ffj1zHTY7iJWkV4yhHGdkWzc64dwSLKmavy3/OwufpTN8aGqjsa7V871UHq+eARXvtVtMAjuL0/ppTeOFbq4XIX0ShUvKpEVOgbb5qtaatR6P/26h6Gp2bk83v6M6DLMl62GP+Tvx59IbdqV6MtceTFP2HbNNtXKsctz6LFc+yLO/4HwnX8UDGKd8daFaOvvzyS4wdOxZdu3ZFamoqcnOtH1xERAQ++eQTveVTzZAhQ/IesOM/LnXq1MGOHTuQkZGB//77DxMnTvSQxL5FjoGOctyck9MyHUYQY1cdxauCD/a4RidWG1vO3kJmTq7kB+6s3Wjir8ex+4KjcyzXUffVHw7h+z2XAQBB/vxP76HMqJ7bLtsaTzVvIyM7Fzs4K3q4K7JYsHhq/k7RqTA5Lt1xPjr1VwL/DrF74HYO2TIKeZaEcsStOnPWn9HPwZOTza17mVgsUDS5fCnhRK7lGzoq4T9jG/kLeel7cb8SLlyFcurvxx3Oq6371+5KWxaECL+zGWtPS6SUKY/j78W1QNiepsXCYsHW86p8YISoiTeVlJ7hMIjYdf4OpuQ9Q38JC9FZESuUkvVMqo5w+zE1VvyPN5zFC5xpPG6xfb4UXzpvK/rfZGkl+7fD4v5xH60/iwqT/pb1K8svx/Eet5y9xfumR/+UgJ5f7PJotHrNytHnn3+Ob775BlOnToXZnD8SbtSoEY4fd/zgiIJBjoXfyCWlZWD8z0c1r7QR8m/yPV7j1/nT7aIji39Uxk95dqFyzIz3/jrF6wi4/jdqewiLhcW+i3dklxwD/GmFfZdS8M4fJwFAVcBAu0jcabW8h6NmlCz3btRcL7QkpD3M5jl3io1A1U5riXXOgGPnIWVqz86xICvH4uCzFX86fzrqiy0XRJWYtIfZmLn2NM7ITEcK4VpdGn+wUfV1Np78eCvP0uIsYoOC+FPJoqvWhHA71YsicYLEfMLE0GKhEcZ9Ou9EPKnHZ2/JK5fFZc6Uz9/HEnHzXgb+OnYDs9edxfJ9fL8wNYqxGuXo6+0XJdufjaeSRZWjzJxcUQVXSum1ITX1ym1HglW0HZ9vPs9rm7j15shVeRnknprUufl5zvTCsAVchny3HxtPJUt+0xcESl+VyFAEq5wqNQLN6zcvXbqE+vXrOxwPDAzEgwfG7HlEeB7hR/vmyiPYezEFPx/6D5dndRO95ui1VJy/eV8yknXqwyx0nLedd8xqQXF+tGCLESLHiv1X0bB8vgP+BE68GqkVOEKW77uCt/84iVplw/DLiBaS6cSWMH+36xKvsa3LnEc6iohen51rwd0H+WZ023tQMz+fnO56Z5yRnYsh3+1Hs0olHOqAmGUnx8KKTicIWb7vKrrXKeNwXFiGmA8IYLUcdftsB87dvI+t/2uLCiWL4FrKQ4fgf59tOoeWlUvao2snpj1C85mbAThasuT4fs8VjO9UVXUU6UNX7to7DAC4cMu4tnH49wdVpVOyEirpRqkPs9B85maeomhDLF4P4LgSz5X9tMQUwyYfbBJJmVeWhUWAgvKTcC0VLyzah3eeqoE1R7VvZTF82UG0qlzS4fgDiZVmXCd4MaQsotwppgCJ1XHOEiWIxK2X74+wLd169haS0zMl/QiFq0VjS4ToIoezaH7KFStWREJCgsPxdevWoXr16nrIRLhAcnoGhn9/UJWzpBKHrtzF+J+P4s79TPgJVv5JdVpC2s+V9qWxrSBzNxZWuiOwHb9y5wFWHrjq0Jhfuv0A2/+9hV/ylrOeuJ5unyoTkmth7UuHubzLmd6Kwh38EfgOtgSO46VhWRa5FhbNZmyy+zDYZN994TYWqIj/M+pHbVNmQhgAfybcwN6LKfhk4zmHlWBisZKEvkByfixrjjn6O6mtV9k5LM7ljTTXnkjES0sPYvRPjsEl0zNy0OXTHUjIG7EPW6JOkQAcrWCfbjyHx976R9W1T0tMXXgzSirti0sOiCpGAFRbxbKc8F20X6txJaKaTj71YTZ2nr+NjvO247PN2kIJAFYrmpjjtdDSrhYpXzrutPvLIsrwzH9OY9Tyw04tSEhKz+DFV2JZ64rIQ1dSMG7VUVxwMnq8GKcT01EtqqiqtGoHqkah2XI0duxYjBo1ChkZGWBZFvv378eKFSswc+ZMLFq0yAgZCQ28tfoE4k8lI/5UsqRFRy22Bn7dySSeM63Y0tI568+gSKAfRratrDp/T65OUJpCaDNnKwDg1I10VChZBH0bxWD6nyfxc95ImNsgXpZY3bFox0Weo64YFU3i5vrh3x/C2eR0B4dUi4XFEJG4JRnZuQj0M6l2qlWz5J2FfKRssY4yO4cFXNh2Su0qOO4KvfM372PjafnVXdvO3kKRADNOadiSQbhkXrgSzEj8kYNsN299yf0mUh5k4c2VCXi2YTk8VTcaFguLwwrTMWI0LF+Mty2NbbCR9igb32y/iDrlwtGhRmmpy+2kPcpWtLoIMdJPkovYtJozC1gA6Skv7rcmNiX61TarFXTAhVhNFlEbtpAbNlrO2mz/e9u/t3Dwrfai17EsK9nm3MsUbzuUpvVsGLlCWg2av76XXnoJwcHBeOutt/Dw4UP0798f0dHR+PTTT/Hcc88ZISOhATFLhasIVxk1n7GJ5xtyPfURvsgL+PhK6zjJfK6lPMS/yffwZHVrY+hs+Hk9sMUhEiK0wi/NCxr59faLSOSYfbnmb27MIS7zNQa14yLV2eeyrKgvRbW316FDjdL4ZlAjp8t0KIsTrVqMjCxHBUtqWkAt6Y/UrVDh+jbsE2znIMa8jf9i3kZt8VNafbhFU3q9mOS3AiP8/kL3zP/DCbaS8gV6wan7c9afxfZ/b2H7v7fwVN1orHYyLk6lkkX4ylFe3X3vr1P2+D6965dVzOcHmT3rpHBX+yIWAkGrlUsJqfxyLSzPb0oslpIa3pOJlcYNHyC0TFlYQGoWnRu0louSn6YNJyIh6IpTxQ8YMADnzp3D/fv3kZSUhP/++w/Dhg3TWzbCCdQaIlmWxZpjN5zSzh9k5SKDs0Sd61cjZQ3afykFj8/egmFLD9qDQ3rSciQVSE/KlJsoEj3ZhpQpXI1DKCtS3h6RVW/cPKWsXs7ExlGCu8GnsNQ5GxwVTFeX2EttKCqH1Lv0VUb4/QUAmOC30q3lmhgGdx9k4bUfD2P1Eb4yJBcuQg6h9cbWySdcy1cofj+irHidk1lBJYW7lCOxKcUMnbdOEn5XVgvyfsRNWas6PpURODt9qAapVYDuwqXSQ0JCEBkZqZcshBvZfOYmXvvxiKRPEHcOWgtiCs/ZJP5GkLaRpBdto2OHYeSVEzGkbsNZs/6H66Sn4vp9vddl64wWuPuHCflLxIFVOJ2g1WtgnYqlwIQxMABmrz+LNccSedM4m04nO2wxoxZhXbV1pkIfRiVWJ2h3lvakZZo7eNQDYVvy4/6r2JoXO2iMTORxozHyGTsTRFNPVE2r1a9fX7Uvw+HDrjmBEq6h9j3tuyQ/FfGpk8ERxQYSRwXbKNhC8SfJWGM8xeebz/O2MXAFZxsOuQU2l5ywrOiFGl+BrFwL9l9KQU6uBS0ql3R5rzrCvSSJRGoftlS9E7sQ4YIG2zSt3F5lemH7/i7q6FCsFr3j8wgHHdyBidTKOHdgpF+XO+qIHKqUo169etn/zsjIwIIFC1CjRg00b94cALB3716cPHkSI0eONERIQj3czujXQ//h6YblRNMpWYbkAoEJOc1xchWzHAmVBBNjNRO/vsJxdZEvIrVCxNmGQ03sFSkeZuUg0M/sUh5S7L+s7NvzMCvHbiU88nYHnLzh3HQMoQ+M6jj11v3xGlcormv5Qp812/SQnzuUo7zvsp1M9HmjWKSwxYZWhNNX3LblkZNWfmcQ1qbcXMdAywUFVcrRtGnT7H+/9NJLeOONN/D+++87pLl2TdoET7gHbpMz7uejMsqR9GjjUVaupu0LXvsxX8kRiwArVBIeZOXyFKrCDMvmv7HLtx+gTESQTGplaryzHnVjIvDHqJauiuYUXOf99zVsiEsYgxbl6GFWrqbNotUg9JVxdhWXM+S6sSwhto1t9UIY44nr5/lA0Ja3Mh3HBUs0ElFCdf5da0dJOlDL8f7fp9C6SinN16nB0zqX5tVqP//8Mw4edDSzvvDCC2jUqBEWL16si2CEc6iNcivXSC3be9np8sWckIWm9YXbLmDNMe0+BN6KXt9w24+2ok65cJfn2o9eS3UIvucuuNMJtv2ciMKLsJ2xKUvObv+jBS0b3Xo7wtW1UhbZx03HsCxgFgCgQsaPqvM3K/iASS3Z/+3wdfx22LmVjN6O5lY4ODgYu3btcji+a9cuBAW5NuolXEetj4dYs3H46l1sOXNT9XJqMcRmksR8bzwVANLbOfZfmi7K1v84O4+7k4ecEa2nV5sQnufmPb5fYY6F5W/XYyC5FguOCfwdCzpNTdr3rgOAGworPo1YCauE0mbPRqPZcvTmm2/i1VdfxeHDh9GkSRMAwL59+7B48WK8/fbbugtIaEO4FP32/UyUDA10SMf1DXqQmYMigX7os8Aa9LFnvWinyxdThDy5asQduHJ/Ylf68hw+1//BCL8nwrcQ2zaFu12PkeRYWPSY7ziQJxzhxqIS40peoFsfbpo0o1k5mjRpEipVqoRPP/0UP/zwAwCgevXq+O6779C3b1/dBSS0IbQcNfq/jeKRsjmVfOrvx/HJc/n75SmNIuQQ69gLuG4E7s73euDLj+vE9XQEIguZCJCNDUUQRlPQB2XuxNavFKYn6lR8+r59+5Ii5KWonVbjWo5WJ9zgKUeuILZCi2ZXCg9lD87E2aA1eCbzHRxkq3laHKIQQ8qRvnyx5Ty26+yw7804vXnPoUOHcPq0dX6zZs2aqF9fn86VcA9CA49eUzliJvMZa+X3FyvMiEXI9mXT9Qi/NQCAyf4r8HTWux6Wxr2UCQ8ia5kXURiVI7H2RC/mrBffcskwfG212s2bN/Hcc89h69atiIiIAACkpqbiiSeewE8//YRSpYxZ1kdIc/7mPWTmWFAzOlxyCis71wI/E2NfcSB0dms+c7PYZZrZeV7fKabCiC/7HLlKVFgQkgzYH9AdjO3wGMa7yZ+GUKYwKkdGoTa4cEFC84TH66+/jnv37uHkyZNISUlBSkoKTpw4gfT0dLzxxhtGyEjIYLGwaD93O7p9thPpGdmiS+nvPshC0xmbMOrH/OjlwoaD2yEV4r7ZK6DHry9m5KIpcxqBEN8gWC9KFnVc+OBLMLCgINU+o7fZCfQrPP4C7gw0acPTNVHz2123bh0WLFiA6tWr24/VqFEDX3zxBf755x9dhSOU4TYAdx9kiUaoXrzrElIeZPGCfPnKqKqraS+W+H+IYlAfNLIsbqERo24qrwlzGg0Ybbu164n4ajXrmXC4f9sDT9KrXrQhy3fH+f2MlYHvY77/5y7l837PmqLH+zWKwdb/tUWIv9ml/D1JEDKxM3C0y8/ImxBG59aDalFF7X+HBPju+9ZKeob7lSNPo1k5slgs8Pf3dzju7+8Pi4E79BLicB2gzSYGYu0Bd6+w+5nWSi7XbHiT38SCgM/Q1nwU4/1W2Y/FMdcxx28hyjPiEV13BY3GL4HvoTpzRTbvUDzEqsD38VvgdPjB2I8/CJl4zrwZpaBuB+2P/L/C0aCX0dx00lC5jEJLZGYbg1pUMMRqOdS8DgDQwXxI9HyFEiGq8vGTWFkQFR6ECiWL+PTUQ3vTYZRl7qC7ea+nRUEFJhE/B0xHW5Nr2wtl5+jfH3FjdwX7sDKslQeZpBwp0q5dO4wePRo3buRHOL5+/TrGjBmDJ598UlfhCGW4DYDZxCj6qwz6dh+upz6SXV7/IPUmPG/U5BPO5FtRVga8j2f9tuP3onNkr6ljkt/fKILJj8HiB2M3b5zqtxyz/Bfht4DpMqnYvP+yeMa8HQDwmnm1oXJ5E55SLdQaUaU+Lds3p7du5IyCySUQWXhKpbLjall6sfTFJpjn/yUam/7FkgD571sJo6fVjDS+jzb/ip6mncYVoJHl+656WgS3o1k5mj9/PtLT01GhQgXExcUhLi4OFStWRHp6Oj7/vOCYZH0F7t5FDBjRaTUuh6+mYsxPCZJK1OOmY0gIegUf+n2jq5yuwnKqaknGOsVWPCvRpTytPha2/I3tmtubrf5eMSbppbC2DsoWcM2XceZpmhjPdNHCKeZfRjR3SDO+U1WHKb9nGpZDSIAZ/ZuWB+A55U6KvuatnhZBM7XLhqO4hil0Obj7jxmBURGc6zHnMcb/V3wasEDztd6h4uqDpxemaF6tFhMTg8OHD2Pjxo04c8bq11G9enW0b99ed+EIZbI5DXsuy0LNzOalOw9QtXRR0XOj/X4DAPTz24qJOS/rIqMeOPOZKHW1Js55o5UjKVnEyr2XkQMEyV9XEDExjEcWAwiVo8iijtsgDX+8En4+xN/2Ys4zdTCjd20E5Dnm6m05crVOBiJbdVpvUez07BDHrtJnC523ulXH//3tuC2HUZajCKZw+Rp6K07FOWIYBh06dECHDh0AWJfyE56BO632X8pDRcsRYG0IPb1vjVYs2o2cioqFOxUPNWVZ30vBwJlna5TLjpIswg1Khb8XD2mEAD+Tg+LGMAwC/LhC63sDrtZPX1WsjR6oqKFBbAQOX00FALStGmlXjrh11ChF3hvu3xvwdO3V3ON8+OGHWLlypf133759UaJECZQtWxZHj3pms8vCDHdard/Xe3Em6Z7iNQzje8v1LQY0GCa3Kkdq0vjYS9EZE+MZ9VAY/iI0kD9mLF7EukRfSTItyt0LzWLVJ3YSX6xPxYsEqJa7WaXiBktjRfq9+t7zFUPLSuDChGblaOHChYiJiQEAxMfHIz4+Hv/88w+6dOmC8ePH6y4gIY8zy1WT0zOx+8IdXcovgkdoYToBs8EOzc6MppSu8AbLEfe+tMizY8ITLsvkbZg8FDaGu+KzffXSKCWIV5Sj0rFXSw0tV0x5hVz5UNe+KTF5qkSG4ukG5VzKV4521SKdvrZCiRDVK/5qlQ3Djy81c7osMYqF5K/C5n6JUhLpNa1WGim89tOIgaAUQ83/4EjQCLxi/svwsqLDHaerufRvGotXWlcyXA61aG6OkpKS7MrRmjVr0LdvX3Ts2BETJkzAgQMHdBeQkCfb4BUZSiwN+BA/BszAK+Y1qtKH4iGioF0xs7VDb7SrrPlaKbjKiPGKkmP+RfEQUUyK7FVizWT8mNaIKa5u+bmxsHjOvFkiTpT25+kun6OR5tXob95k/z3nmTr2v5+qW8Yhff3YYtY/FITTspR/cPMKaF89EkH+/CZ49aiW9r9jH53Gh35fQ+pZ1i4brro8G/Fj26B3/bIOx7mLE1zB5EK/bguVoGYgxLL6T8NWKFnE/neI5T7888J7SL1XKReGIS0qiB5/vEpJh2PNTKewL+g1LPWfZT/mzmm1af7LAFi3+zGaSqVCZc/P6F0bk7tWt/vwta7i2d02NCtHxYoVw7VrVsfEdevW2R2xWZZFbq6x1gPCkRwPx5ZqZLJ2jM+at6pKfyxwOPYGvY5IlfF+bNhWq4UFO8bYcoYSRQJ4TdBgiQZNL8Sau+NBL2FBwGeyacSoIuFM725amk5YwxMETleVfsnQxuhUszRvhM7FxOg7UTG1a3WHY+WZJEzwX4UZ/t8CAE6/1xkda0bZz0dHBPPST+pSDWaTbcsdebR0acEBZiwa3Bj9m5TnHa8XE8H73c9vK1qbxLck+XpQQwV51D3NkebVTq2MkirVWfw0alZ6x5Wy6TolkIblt/tiU8A4azkS6aUC6TarVAJvdXOsex/0qu1wbJB5AwCglTk/npkrliO9FCsTA3z6XD1d8tLKjglPYNGgRqJKvDvRrBz16dMH/fv3R4cOHXDnzh106dIFAHDkyBFUrqzfqJ5QR1aOd8x7q/0oTYxV3vqm8wopBfnn3aYah3MpPvZfgC/8PwHAIjzYHybOaNmVEa8a1Dlke8e7VGL0k1UAAHHMDck0Yo+zbdVIfDWwEYoXCZC8yqbQDGjqnE8O17LYPK6EgyxFwQ+TEJwX5Xjpi00w7akaaFyB78fC7bC71na0KnFxpq/2NytfFIpHEtc6Nw8pXIwxwX+VRErtuKKv+Ck8C+52Hc42A2UFyq8YLfMCr8bmhd3g3hNXQrGtmmzpX3rccXoopngwnqobrVi+NzhkB/mbUb1MmEfKLh0WhPY1SsNkdKOsgOava968eXjttddQo0YNxMfHIzTUaipLTEzEyJEjdReQkMfT02o2jP6gbavVtMzzc5WNEGTgafNOdDPvRxlYp7K4Dtkq+iiX8BXFRw1mJxqtSqXypyyknoSJAZ5uWA77pz6JaU+Jb9WhxJgOj8mel3LCb/NYKQxtWdH+e2yHx1C3XDieb5KvpJUMld87jVH5DXA7eT8T8JL5bzRhHJeK25D6tvwVnLQ80bW4Mi1qdoPTWc1o5Q4/V9AtSr1XrsWRi0lCQ2QYBp8/Xx9TulazH/PWViHI32zoFHfJUKkBkvegeSm/v78//ve//zkcHzNmjC4CEdrQe1rN2e9Bq3Kkppy65cKB29a/baZmNY2bGFzlxAQLwAh8jnQ00feoG40/j/KtKr66Ws1sYhymD5y6FxW3ZutUIosGOb33H/c9ir1StSsU33iyCt7Is5CpL9vx2JrXW+F+Zg6e+zo/UvXeyfk7CVRL3Yan/JcDACpk/KipPH8/5+psnXIRTl1nNFp0bme/FCnFhZung3LEiP94r2dN/H7kukgZ8jJwF9GIJbWw3PJZiVTGEuRnMjTcS1ypUNy+L+9v6WlUqep//vknsrOz7X/L/SPci7dMq2lFjTI18on8KRLbXbaq7OjUKIWSwzVPYRKI0yA2QnU5QlpWLqHKfO5uFgxooPmabwc3kjynpcnmTYdKVFlux6WHRT063HEKhV8PjP12zv5fZ9QqG47qUXyFPpizYWnxjGvCy1Qx7aka8FO0HInfX3iwP45O6+hUuTZm9Hb0n3EV2/t39a3UkJkOkntkkXmrFIXKkRRFg6R85xwrL3c6L0dhhTH3rJwyv3pUSzzT0JiVh0bsExjAnRbVPXf9UWU56tWrF5KSkhAZGYlevXpJpmMYhpyy3YzR+wepxYhpNe6cvs0hW4+Pls0zlJt41iR+vu2qRdqDwDnD58/Xx18c65GzViEmz0crpngwrqWI+56opWWcOsWyfmwEjuTdu1IHLIXwflkAsFiA3CxJvzGeb4fEe944tg3az90mem5C56oAgM3j2uBRdi6K2X2bxJXkhU4oi3JwRX7ticoI9LMqQYzgEXI7zzIR+asO6wqcseWoVCpUlb+SFOEuLmzo3zQWU34/7lIeQpTuhqfWysz5mJGLOX4LkcBWxvJc/s4NUvWqRJEAvN+zFuJPJctajqLDg3BUSZ8VKSKEoxArWfu5bakJFocAuDWZy+hu3oN6ka3xg4IorhDgpE+bkGcblsPBK3cx/ama2Hzmpi55ugNVd2+xWBAZGWn/W+ofKUbuJTMnF2+sOOK28hhYUI4Rr9xGjAS4Myt6xv5Y92ZrAMJpNX6a4ACngsfn5WvN7ElOzBcjHLJ/ellbnBdhJy1FiSL5vjVy/kVa5LWwLPDNE8CMaISw4nvHiTlgxjDJWB3wFrqa9mKc3yqUX/sCRjwu7qw9IG/lV6VSoagZLb7MneuE37lmadXyAwAubkVtRnozY65vyqAW5TnH8/FDDs+/rRJn+fiqV8TfZ1iwPyZ1qeZw3LGjZ9HcdBKlkGr/zcUEC1TtL6RAm8fkllg73xLYlEatq9aENMvei2f9tuMD/8WSZQhZMrQJosKDMKJNHMzmfEWmDnMBDFh8N7QxJnSuisdVLC8XK+Pt7jXsfwf5mx3Oc1GKsfR34BS86vcXsPFdkbP6tZMVOXXTFZ5rEost/2uL2BLeEH5EPR4Ku0a4xL1k4KcBOLvLvdOYH/gtxs7ANzEwb/lpHeaC/ZzQclQl0uqoXwJpeNX8J6fBVg93GwfngkCKN9RhQX5gGIZnORJ2NC45aOddO79/Aywb1oR7SOEybR1Ls0olNKU3Mwwm+a3AGL+fZdMVCcxvvMWUo9rlrIrHdP/vJfMQXmWxAEhMANhc1M09IXqNsKgBTWMx028R6pkuYkHAZ3jdbzX8L2/BpDiJHcJVPORfAt9TTiRG2nXg+574K/AtyST8VU3cKULr32F4gITAl2Fa+bzo9TZLk5APn66jajq5rSkBKwI+wO7A10XPXwx6AfiyhSav6SBkOhzrJOGI7DKMwx+SFLHcB+LfQTXGsS6Esg8kr5PSu2wG0kldqmHhoKb2438Gvo2wvXPwRNVIjGxbWdVqPGEZzzeJQWuOQskNG8L95rf2DcSp9zph5Yj8WFcmufhTSWIhHvQbpjIMg85GvWsfwCnlaNOmTejevTvi4uIQFxeH7t27Y+PGjXrLRkix9n/AmTWos2WIW4vt77cZAPA/v1Woy5zHn4Fv288JlZcZfaw+CV8HzMVE/5+wKOAj3nlb+h+GNYUUXNO5suVIIUCf6DGuz5HA6VjQCvbXsLTcdmVwgJkz0szPf2n59arzslHGchN+eUHpnMX04CZG+P2F0X6/i3Z6NrjbZ4hNAbR5rBTm96/vtBxSzb1wxG02MQhjRKxMuVkArNHZ25iO2p+LZoODluU46dJhC2zwpwUd/+5s3o9QJgPMv+vEE6rMGxCvz23y4iH5MzIW/FungZwMVWWO8fsZZ4KGorXpqKr0rmJ/fyoeyYhH3wC7PsW6wEkO5+TeqpTliHvcbOZbjUMPfmH/W3h1GBw3iRWWUURghQ4N9MN3Qxs7XFfhz6cREuDHW7UnO2BSqL+uTLvaMDO5mO//GYaZ/3Y6j0oiFqh82Vgs9p+Nb/w/8ro9rTQrRwsWLEDnzp1RtGhRjB49GqNHj0ZYWBi6du2KL774QjkDwnXSHVdIuBMWjD0WSP4xPraq39B0DgBQ13RRdEquapR0QEPu7uhylqNh5rXYHzgKFZlEgQwSHxvDILZ4iEA5YoRJeEzoVFWyfMfsHWXlHmmTvFT8Oon8mptOYlXGK1gR8H+qZRDN35K/S7s/Iz0iDQ3Kb8wzcxzTMQyD7nXUOZyXQipMsPAULqlNhB2sTSwr2zl8FzAbSwM+xJt+vwKQX4kkjpa4EPy8W8tOLfHvJd/RWEQ+lR2CmjABwvwlr1Aosy5jjUE22u93AMB0P359NWqDYNtzKhaivMw7Lld6elPu9qRk59Udk/y0l52jP+FY0MsY4/cLvwx1V8vA9TmSU47EvuH8a0+/19n+9+Dm5UXSKlP3wR50N+/F23krKp2hmEhcs9jiRdCrXjRKIQ3tzAnoYD4MZKQ6XYYRaHasmDFjhj3WkY033ngDLVu2xIwZMzBq1ChdBSS8FSl1KO+XSAvxuf98h2PC0f63gxvhUXYubqQ+4m0syYKRtAy87W91S5zm9z2GZE9UlBwAZvapje9WnAQSxeVgAHzSrx62n7uFWX3qaIonpWSlkr5OPM3zZqvFrrFJbJsO9XCnyMwMJHUDriNmZrZzPioMWDRg/sVvgdOxK7cmSj6/HlhoPSfVeQmVSqXV/E1MZwEAfc3b8FFOP+n4MlI3qmmkmp/3R8/WRYca8v5K2hU1+bL1DQEkf99/BL7DCysg9AWTuzNXFkxEhVkHQ0UC/QCxmTGVr+thlrTlTOq98HyPTdLdIu/yNWMAAKP9fsO8nGc4aVx89xznQNlpNYUH4se5KWe3G7qbmurUdTZWvdJc9DjDAJ88Vx/vsynAWZeKMAzNn1xqaio6d+7scLxjx45IS0vTRShCCYOGbhpKV5ZAxLnWdNsxlaAhebJ6aXSvE42XW8fxzlmVI/lShQ0Jw/ub35CUDgvCpM75AQMdGjSGQa/6ZTG3bz0E+Jk0jZaFaVcMb+ZSDKNsqBzJKmDimutl7of7LFwJ1jbAz7p/WUvzSZ6F0MJKTW3wf3euGaWppmvvkzRFFLXzTMNyiqu9eIYIHRQlqTxWDM934na0HOmhFALli4eoDvw5RWTLFrVMEdlyQwo5S3Lqo2zJc1JX8b5/Rvi9aXt/ah6VbBJu3VGwHGl5lYff7qA+cR7X06Sn39XQuEIx2fNqA6d6As3KUY8ePfD77787HP/jjz/QvXt3XYQi9KN9dfldsoe0qIA65fJX9qhyfGYYh4ZX62oy29Vq/UQsYJyKzCxOXj4cs7TwQxCW5EoH1zyuhEN+3KW9+WWKx2LKZQVpr+4FUqSnFaTgPj650a2ZYfDd0MZ4q1t1NBJspyGFcMdt20SSGFJ1TPiMH69SEnFim1VK9AhS22k4O73Ez4STt4rruI2+bNVRU68YxuHebJfZtkgRl0FKTm3KEQMWOyc+4VC2GBVLFkGPutGICgtSnHoUYotAbmR3KSW7mafNSneLvM5coh7YLG0d86yLA5ppnNJi1PocWTQFahRu21NNxqXBXoTM2/jseWW/Q6l2xvbojJqi1QPN02o1atTABx98gK1bt6J5c6vJbO/evdi1axfGjRuHzz7L30jzjTfe0E9Swmmiw4NwI03cCbPNY6XQpVYU+uVF8HXWwuEwahWp9KGBfrD5AdvSqx05sBLKUT1Geo826YB/eX9zGjeHaTUXPlqxa4XP9a/XWwFfCNOIk8NR3eKY68Di/tYf07VZahlOo2sWOKA/07Acfjn0HwCgU+AxVFv/AZ7ovRCA4x5RYvSsXxZfbr3AOyZ1P2odshmGQbC/8vjNdifalWfnptVUbQmvs+WoQokQ9K5fVjQis10saREECbVPlXKnWm2fzbgOj+HjeMep3k+fqwcLa/UZO3/zPnJyWTw1f6fmMuVw1g7L1Wfm96+P1360hkLhvSO5mBcSD3X44xXxzY5LvCRfDWyIh1m51mlCYTaWHFRnrkhYhrg+RzLvSsN7FCopL7WqaLXyKSze7F6vHHBK/Fz76pEwwYKSSMNNyFuIpOXi/XIqD6PQrBx9++23KFasGE6dOoVTp/KfWkREBL799lv7b4ZhSDkyCg2NrdIglwXr1By5aJA/3nlHgvzNEC6SUht7xyIxrbY68B2ODNL3MaptHGDbwSHlIhBejm85EnbMKpQ9KcQUvgA/BuC4QohZRKQU01zOtNpjzH/qBYF1KfYQ83qssTQDv9F1ZPekdrh85wGqLYuzHvi+FzD1BooEmPFAxo8DcNwQmIG0M7XUexKvCyJ55GYBkN/nTCGHvBMqu9gHd4DNXGd4brRvFnh0FwjhW9jENitVtMrKKF0Mw2Bev3qyylG5YiHAPevfZsi8L63KEcuKfnuvP1kFfx9PxJmkew6ymhnADAbVy4Th5A393S30CDpbl7ONilqfLqlS+zctn68c5T0rhmFEFSMAeGz/W/gn8FfV5VVm/kNl5gbWWZrkn3RuVjhPNvG4YkJCAqWn1RkwWOw/B23NR/F81lTssTizH6J3KURcNCtHly5dMkIOQgNZuRao9QRRs4u93DeybFgTDPx2P++YWOfvaDkSy9TxmMORjHRr4x0c4ZC/0k7t0RFBwC1u3tZ7L1U0EK+0rpSvHC19Cui1EAjL32XdcSk/P28to39eUksukJPp6DW0bY7q/HI4V+co+B+9YI7HRbYMdltqAQCOBL6CYCYLk/ATwOR7PgpX+bIsEB0RjGjuruXZVq/Yzf9ri6PXUvFv8j1UKS1uiherZlqVI9FnLJbF76/gKVP+gpDQIH/88VJLkYQ2OaRQ2bv8MRI4H8+5zALY3sMfo4CE5UDFNkCHpaJliladjHQgKIyf8tQfQM1eqkQS+wa5cZIW+8/BYYvE3nBOLJnm3oMeC65falURi3ZK9CUS31rTSsWx45zVbzE4wA8QGMOL4BEqlIkEVAZhtikHAchGwO3TQEQDCbOv8rcv+b4z7wEBoQ55lLmkrBgB+T5HGwMnAACez5rKOev4JqTejegtpEkr2vb0nBFLBO6hKPMQ19jS9jRtzUcBAEPM63nKUdXSRfHjcOkwLbb8he2uN+H0GoisrCycPXsWOTmuxV4htHMtRTzCsBg5Ckt+5GYI1r7xuPj+QYxjh8Pt8N7uXkOiQ3IM6siLL4JcYFYM8GF5ICeLd2XX2tEY11F+Ob0wngZPBmGHsGc+75hQmXMYacmWLMPCx4EZZYBcgclsi9iyfPF31bNBjP1vOefsxswZ/J//d/gxYIb9WDDDfY7SfjBvtpfeZLV0WBA61ozCa+2qSAYAdNzOgRUmsP8ptbWUuJIunvjzgPyVj0UC/DVtvSEmkyxX9wiu41heEvKWOF/ahogzK+yH+RvgMuhWuwzfWvjLUMdyuAoYD5VTz5xkbcxiAQJtCVnAkosni1xCILKk0+Vf4NIKrIy8FY81mcsYZF4PBhZM7VYdf4zKV2in+y0B1o53uHac3yo8xlj36/ikXz1sHNsGo5+sgjIR/H3z6jHncTJoGFZFr0BLk3iQUeud5GPzM1ri/yEil7fLf5cO5N97++pWxaBhef40Ercds09BXtsPzCwH/KV1BiVfyucal8XQlhXsvyf5reAkU2cBZGBBpcS1wB3+tDfOruX97Gg6gPamQ4KL8+8rIegV7AgcgyjcEZ5yGPBERwShRKi0ddfucyRRljegWTl6+PAhhg0bhpCQENSsWRNXr1ojlL7++uuYNWuW7gISjmRq2Gz2xPU0xYZN6nyN6DDp1R0y48dhrSqK13ORj5mbrgg4e4c95K9sK18ylLdxobhQcvcpkJcx8eQRWazGQ4s/C+953jwpnVB4ncixBrERKF40X+nL4Rp7BQEayzG3IAtHLuGIzdmlvnZRRC1HHFREOzdq5YorKwVFkVCqAu+c4ZTJ54sBDfBq2/yNlHFeJGiuzmIyUqNy1gLs+hTf5k7F7yW/UpkX53KNcj7ItA6g/w6cgvf8l6K3aScYhrHHMwrDfQzx2wDs/xp4yN+p/XW/1dgQaA3PUSI0EJUjQzGmw2O8QInWdNZFQkVO/ohe5t0OMnStHYVRT8TxjtmyaGHOcw/Z/43iDRYrEoAz73fGz4Il6tznY2+ntn1o/f9h6UjySvyvQxXEFOPsv2fiLMRQ+SJ6mXahzYkpwOfyewl+HTAPiwI+RhAy85U9h5V7QF2TVcmS+16N2LjW3WhWjiZPnoyjR49i69atCArKX6HSvn17rFy5UlfhCHG0tE05uazsJo2A/LhU3LnYseG1/arFXAT2fS0+qhGRgzvikv2c1Don8eQU+IYI8+McE04zOfocaVCOVKdUxt9k4sVdMflxJlQt/CXLyn4YXCudvjzbiL87uNjEqw3uUv4mKlfDOUt1mR3a1X9JwjlI5RG76urCS+iqdqTyG2EtwIFFAIAa9/coJAbA8muWlhVSgHVvOC7VTFZLkO3W/bhOx6r9oYSWXnmZFgxoiPGd+PvTmR1ekrr7CvI3503Jiae3W47EFGA1cNsq1iId6VrpWaVcRC/TTjQ1nXY4JdeeBSHL/m5YkXbXDxY8WS1Sto6rHUt68/5lmmVbvXo15s+fj1atWvEecM2aNXHhwgWZKwnd0DHMOsvK+9OEiGzA6mc28UYzVqx5rAl8C/hnPIpdENv3zcnVQaK/tSKiHPEiZOtXnLODJskGnqMcze7bKP94rpopEYksdR7YVYtyVEL4yml+Q16+ZP70kmJH62JdXzasibTjqdN5a1vKrz5b5wJudqtt9Z2rXTacd1xSAtYC+Kl3aAdcW3FXLyaCt3Gu7NNz8p3IxgMS0NaUgDjmuqOCYH/+rvlkBfiZgCzp/d00wVrEXRvyznF5pXUlhHHTflYfnwQswDPm7ZqKNHGHWSLKkRm5WDS4kUKdkK8vj5W2tgH8sYF3+R9pVo5u3bqFyEjH2DkPHjwoEKY0X+CRhmk1ACiBVNRgLkuel3ttlSNF4swAeLoB31IgtFoE3T0DB3hTK1ZUN7pOWI4cVhVxMZkF02ryPkfuQFw5YnnKUVQxzvvIlQ52J05+/v0Dd6KveYvG612A86y500tR4fm+I4Gi06Yq6rpMHSoZGijTyDnZGEsqMVwfNrHzCrVKKl+Fb2R+//o4Oq0jShVVqfCwFsAvSDld/gWqptV+G9lCMocRbfKntOxhPGzWCYfUMvdrsQDZGQ7PRO3UaczDU1gSMBubAh39m7R5M0sT4GdyaeACwaBCMhq7oL5M7lqd559kw09mqyAxGOSvTmRElSMLGIYRWBMFeUg8stWjWmJ8p6p4IS/2Ez+ZdylHmlerNWrUCH///Tdef92687OtU1m0aJE97hFhHAcvp2iuQn9lvggEAk9mzsEFtizvHAu+gqJ+iSxfCocgkKItqEgzyLmM18A5OAEpK0fyipa8z5FDEEiRvMwmBrl5zjWvtKmErBwLvtt1WVEul+HeO1euHOej176UsQTwB9bnNkYaxBVgV+G9T85mq8WKBAJ5G3YGmE048W4nmBiJpcW6jCalfG9k8s5+BBz5AajS0bEuGjXC1ZBvXGS+HxrDMIoRu/nlKFuOto9/Avjcll7YRojTILaYxBlB8YLFGHJTsA4s6wlc2gEUr8g7rFY5KptxLr8U4fNOPu70N2VrF+oz5xCUWgE4+aP8BXJwxWItkuEAxNtTDYNIqSCWYO0vhRXxOfLL29hYziFbylhbLyYC9ZxZPOEBnNpbrUuXLjh16hRycnLw6aef4tSpU9i9eze2bdtmhIwEhz8SbqCHk9fWN53HhdyyygkFxBQPxrWUR/yDgg9LTRBIJZ8jV2EAHJveERBbF+Dgc2TmHRN+zGIfd7tqkYg/lQwAmNylOr7Zrj1KtRyST0KqwROsgFPsHkSefxCykKZ0nZAza4FQx9GsP/grV3n3s2qQ1BneprQArNYBTZuJOVmH5Kaxts4Cdn0CmPwdfLvs1wlX/3AlckYkSXnyMrt7Gfv6MbhZqiXKhAeLJFPpQ6PCchRbQsZB30XlUGg50sSlvCkiQYR4OeXIBAtw8zRQqpryN7L3S6DC44KDyoJm51pQjbmK3wOnAYumKaZXjdyzdnIaFlDwM+VYjsQaQtsUptxMkdppZUaNSdJDaJ6raNWqFRISEpCTk4PatWtjw4YNiIyMxJ49e9CwYUMjZCQ4mE0MhO6RaskWbkORh1Ij9eurLTD7mTr239bVGErlKlkBHEeNsqM/NS3phU0I264Q8tWGYFqNq6SF4YGoLH6ChkIqhpSz37i1TLFlXxINiMU6gutkOoCxfqtUNEdOCJaRbnUszc1TfG6fA356HljUziFpK9PxfJHBohwjEXBGbnS77ytgdgUg8ah2WbnkZAHHfwHuywS9WfOm9LmLW63/FypGAADWalkSrP4JunkUzUzWlU+inYOwDl/YzO/gzkkt5c/j07oo/cfzqP1AhRO1LKxGnyPBtJqLpVvs3z4jkZ92rUmu7XjP7ztgQTNgOz+2WLGQAMctNK7sFq3bPFgW+OEZICc/0FKuBWhk0msHVRl3AKEcepXDwcSdBxB1yJYPCAsoNNd3LwPL+gAX3Dit7wROOYvHxcXhm2++wf79+3Hq1Cn88MMPqF27tt6yESIIK10Pk+OyVSlyRdYosSwrr+UnnUDkmhfRNybdfki4jBYQmXOW8p+x/6U0enTSGrA7f/uaonkWiVaVSzpKyDDAv/84FFeLuYhjQcPR5OBYh6xfety6lUZniVg/dlgLsHpU/tJglbzrvxRbAsbyQxrIxQ/KO/dVwDy84bca7cxHNJVnI1ZuGf/yZ4AfngZ2zrP+vntZMil3q4Oqpv9QzyRhWds+2/6nw/v/ZwKQkQb8+XreASc7gO2zgV+HAd88KZ3m1B/W/984Alw/JJ1OCMs6LDkHgOBbR/FTwP+hJNLUWUWW9QYOLc3/nZkGrBqsfN2Kfo7Hbp0Fdn3KOyS98axFdud5MfS08MYUL6KcSCNyDtkv5G2AjC0fgNuumEwM1r4hsBKdW69cWPoNh5hUVSJD8VhJbU7uqmAtwI6PpU7qXx7yHLLzHpPYNJ3YliayPke3zwMr+ud/Y7+/ClzYBCzrJR1uwgvw5pV0hAhmhm85etasfirTqd3dl3QFzv4NLOnGP64wrSaKyEiHFyxPtVDqUg5tWR5Tu1bHez1rik+rHfnB/tNmFBrmZ1WYyt7Y4JBfw/LFcPjtDlgwwGoxkPqsSyduARJ+ANb+T5WcNrqb96KiKRl9zDsEZyQsRyx/BBfJpNr/frt7DccCJEaasv3etX3W/7sQq8WBc/nPtoXMxqkuYbMSpF2VT5eTCXzdFvimnfoVRqyEhS+PUkyq+rqcIpiaO7Va7ZV8FjRTn9aZCNmuXc6jeF5wQPGVis5l7pynpLotNJRzseYzqHEZkbR55GQCx36Wt2Tasxes8twkYQ134UUwbK51ICKCieEMmJ20HOVwI72ueM7ah3yTZ5G7l5gvh1Tb5gWQcuRjuLK5Zo/65WXOSpCR55Hy6K5CKWr8HeRLU70cV+UoNjzIH8NbV8pbCivikM0t275yRj7v4kUC7A2q1LSaf7ZmLx4eDo0P934tnHMCnwOWEz9IGHdIDlVP0za9pFMDtntSOyx8oSF61ZPwgbOVo6Y8pfoglwdnaoRfxxV8PZRih6ncPkcVauq7WIBVubQa3yPPIZtlgeSTwN0rmvIQSqYQvF8TJo0rsjSh1momt3p0ywzgt5eAbzuoyIiV+FuYzPkHGPnwnOQ5BhZOnCPHAbVZbjPcPHK5L/fuJcl0tiX9eaUp5utONDtkE55F60iH29EWCdGyfFcJhc5B9BKla+QaBUbibzlk5u5N/I/emWkDyduxKI+s5HBUErmjK07eMuWI34+U5UjFvatYmqxlM1CHfdxkcjUWjsxql1+zFjhV/70FJzae5VaRwKwU4Ms8K0DEX04IkKcc6akdGYjju5R4u3L158wa6/9lpqVF0eyQra7miQV3tMFrf8RW7YpOq/HTZVtk2l5Onp1rlgacjJVpNGQ58hESrqXi5r0Mh8UDSh0Szwph0jEusjPTajyfo3yWDWuCiBB/zH6as6uzXKPg3FIgQR78Z2FTzPRorhkVIys5HOb0ed6w6qIJa3lCqtLmKu+hqPs2HYDK0bELqghX2VQdN4pVYTlyXiTNrJsiLoOcz5FGuAp0kUf50yImVvtAwObuq+csiuq6Z6Q+5lJsIw6s3CCRe8r5dsbCSNtFzLDIhFkAWlWKEDnKT5mTKycbx+9Lz/lanSHlyAc4ei0Vvb7YhSYfbBIJeS8Pd3k1yzjGQhHWR1VKzsM7wM65skm0WI4er1IKR97ugCeqcoOLyn0oKp8BKzN6kbQcyeSd9QBYOwG4vBMAf+RbEmkYZl6LCNwD40KjBYiNzKSUI40dkyuNj14Nv7fBfSbce1QcsTsxrWaUxrT3C3EZZJUjhbqQxd3cmp8228wJ3AmRuEDCZyfYRNr2HGzT0g5tzgOFPQJF0BIh22UyUsWPG/GNaLEc3b8FbJ0hnjaPcNxHH9N2mFnpgYCJM60m5jD9eGXxLX+GtKhg/5u/4bnaqUHvUo40T6v17t1b9MNnGAZBQUGoXLky+vfvj6pV5XdQJ9Sz9+Id+99i02omWFCVuYYzbAxYgb7LVY4selqOBPj7mdEu5zDniEhFl7N0MIy8MsPA2sge/VFiebUS0qZdAChx5zCACHnlcOc8YP9X1n/T+X5F3wbMQV3TRbQxHQXDOhuJykpP8y5pWWV8jrgEmoFh5rWCo+KNz3s9aykLZX/m0g2Ylmk1RZKOuTw9mY9Mo8st49ASoGRVoOnLCtmxss++cYVi2sRTxLnnKnnVpR3KVofVIyRP5XIGWUGWDACcQVduttXBvUQc0Pd7YP1UYM98oP/P9iS2NioqPAitTUfRQbgTvJTyIYOUIjjU/A/vt3N1VHDNbyL149xGYO8CJ/IWQ62SIEi3bqLiFctD5qKW5Qwe3DwgmYZvORJ5XhZHK3LnWlHo3qMmluy+DEDBcqTrfoLGodlyFB4ejs2bN+Pw4cPWEOIMgyNHjmDz5s3IycnBypUrUbduXezatUs5M0IzwtVqAPC23zL8EzgZE/ysG//unpQfp8PMmVarffZzBCILc/0XoIvJugqpvFywNw2YGAaLAz6y/1Yycz/ToBzmPFPHGpzt1J/Ag9vAw9ucFCLX75kP/DVag1R5eTy4DfzyIv+UYM698d6R1ivkxL7J38CRm9S2W3Zr83EwLnbq1fM25swvRcLnSEZYv/8rgbf9f+AflOgQm1Qsbl1F82k9YFaseIa2BtGdpu+E5ZBuPDnPJO2qNXCkM3Cf54FFwD/jgfRE6fSAomLxXk+psCZe4om0bmJ+HCcpbGEOAJF3nv87mOWEnXhwG1jSHUg+kX/9nvnW///4rMPVQf5mfB/wIQb6cZxOJOrXaPOvsuJKWY6m+S+TkNwFkk84Hlv+tB45O3LOcdWsJKkKKzMB1LJYt3UqclM6dMXT5h1W/YWVUCVF2jdhDLgcWX8yqdW33qUoaVaOoqKi0L9/f1y8eBG//vorfv31V1y4cAEvvPAC4uLicPr0aQwePBgTJyprsYQ6uIq2mOVoqJ81NserflbnSK6jK9d/JfLOAQw1r0Mf8058GfApmlcqIb9reXaG9DlHKVWk4aycq1cWzzaKsS5BXjUQmBMHfNWak1TkQ7ksXOKukvVTHePYSDgkSo4sWTbfqZJzqBJzA9P8lvKOu+pz5IBCEEjVyKXf/pF1VUlGmvh5p/a2c5H/Dko3mELr9eGl4umUEFN0sh5AvhtV9jnSFY/vWSmce89/ZsHgtBFz4oBre5Vzc6IujfGXVo7G+q1CfdN5dRl5/FmqgFu3Nr0rm/TZGx/hclB/NGJE9rJ0kpf9/sZj2WeB2ZVQ7rLYcxer+9bnGuxvnZ1oVokTokPGIZuX199jgZOrnRHZEDRPq3377bfYtWsXTJxAgCaTCa+//jpatGiBGTNm4LXXXsPjjwtDsBPOwjVt/nf3EbTEIRdacEoy+Z1f51pRwNYPEWEpBkBkSfUnrgT21Datpup6zSLk5ZF2TeSkeCMpqRyJOOtaWBabAx1jGYXek1666jIyDtmKT0zk+bNgrG2ViKmchyeUI+vwVeIcf288nPkbaDRUexliCqNiaADtS+E9gSEO8gJsJdRktNR5fRWUN/xW65qfMWi5Z/XvrXmqdUD8S+B7ABprE0mGCfdmApYUxF7+2fGkWN3P+2Y2jGmNjaeT8VxjrgVa5f2cWWP9V9O1UCh6obnFy8nJwZkzjlrqmTNnkJtrbWiCgoLULQ8mNLNi/1VePJvW5uMyqR0byJ6cuDLh9y8AW2cgevt4iFbgByoClkmWK4IW5zsPdT5yHjVq05a8vU8nafLgKiY8nyOtDtlisXBYMLlZwMFvlYTQVpbh6CSPM87zCj5Hkud8tU0UfoucKRNb+1KOuQ21yPv9GPvdO2w2qwZd3punVnI6R6Bs3CjpcmOKh2Boy4oIDpDzb5WwhHsZmi1HAwcOxLBhwzBlyhQ0bmzVVA8cOIAZM2Zg0CDr5pLbtm1DzZo15bIhNKDl21TyISpZND/WkYmzcWkwMvEIQQaPNlnRPxXT6lGeynOaHDYlP2y9n6GUz5HQcqQgu8S0GqMmKrNdQXNnY2bMaJt/mYTlSLbqWJxTjoxApnMxShVjRaeN1T9/2XrqxZ2l2/CCZ1A0gAEeSZxUK9+DO3l+gwJ8ZJCg2XI0b948vPnmm5g9ezZat26N1q1bY/bs2RgzZgzmzrUu7+7YsSN++ukn3YVVS2ZmJurVqweGYZCQkMA7d+zYMTz++OMICgpCTEwMZs+eLZ6JjxI/pg3vt7Aaci16OaYA+9/hULl1gkoYsQ9Iq+VIZKNaTTjRyEheIZKX25owqThH92/yNnutI7WXmf1aCeUoR2Q5tpDcTKvD9o0E5bR6IVzBKDzHxdkORfQ6FXXNrcqRc53JCD9nAjSKwX9GYgMoTUvpZTtHzysGjnhrZy70BdPv2ZlYuWl2kXIe3gFSBO3PL0OA+LdFruc8z+uHRc57B5qVI7PZjKlTpyIxMRGpqalITU1FYmIipkyZArPZakqLjY1FuXLqty/QmwkTJiA6OtrheHp6Ojp27Ijy5cvj0KFDmDNnDqZPn46vv/7aA1I6j9wnEODHf6UODRlnioa7qiqMeQjj0bIyQcdGUlOjob4hdIvhSKgkcjveX4fhh4CZ9p9FGamhXh6uLo2/ewnYNkv83IXN6G3e6Vr+mtCpw3La50jmWQqVozVjgC+aWvfX0htPWBk4ZTIsi0BkoSIjssJPor5ZWM9ZjnQNN2EEKwcCl7apS2vks5JrK8SU/4tbgc/qWzfltXFpu3I5v72kWTR34dL2IWFhMiudPMQ///yDDRs24Ndff8U///BjXCxfvhxZWVlYvHgxAgICULNmTSQkJGDu3Ll4+WWF2Ca+AGOyfjByjTtn526GMzqwxUPSrfFQzEar5cgZZK6XGN1L37+YJUx8dBX6UMwB3BVcWKHGy8bxWrtDtqss642n9A6jdXCx9DkHoTnP6FcNDa7UFgyPUuSv0WI5st3HaSctOVtnAkVKOnetHgij4XN+F2Xv43jgeAQwIvVSLB4Q4NFplf5JLswUXNwG/PWGkxervOfTf1r/qUJuFZiLyC3Q+O8QcGwVUKev47kbR4AwR8MEj4I6rZacnIyBAwciOjoafn5+MJvNvH+eJDk5GcOHD8eyZcsQEuLoe7Nnzx60bt0aAQH500mdOnXC2bNncffuXdE8MzMzkZ6ezvvnblQ7t7MW4N0IYHo44pjr1mtlFITHrv9u/1vNZoJaEAmsyufHvsDu+dLn930FzKnEydCFD0rsWm4cFw48sVOvARveAhJWAH+Pc0hrclfUaCnLkVZEYgG5Y0WTKD8NUI4nJItMfTgussJGCrFFB7lZQPp16WtYVl5J3fgu8PNQ/Ub2Nw4Dm9/nH3toU948YTnKr0eD/TeIK0YAcOIX8ctlVz5647RaHt/30L43mh3OfZ1Sq/woZWmk5UhGObq6G/htOHBtv+O5n/oDZ4TBZ4X4hnKk2XI0ZMgQXL16FW+//TbKlCnjNavSWJbFkCFDMGLECDRq1AiXL192SJOUlISKFSvyjpUuXdp+rlgxx8i2M2fOxLvvyseacDdqrDvz/T9Dl6wPZdPUuJ7fiTSrEI5jLq1AlxnJS7FhqvS5/V+5IgxfBA2NCO/ZftsBuCfRge/8BP1P/ui8bM7iinIkca1HvuEza6xKyAANigwXvXyObp11PHYvSf4a1iLfeVzJm15sPgoo1yj/uCvPOUsw7Z2YAMS1E01qJBG4h34HX7H/rpamfSqV9eC0mlPo/XmsGqhTRgY+KzUrYW+fEz/+0/NAOf3CCngKzcrRzp07sWPHDtSrV88AcRyZNGkSPvxQvpM/ffo0NmzYgHv37mHy5Mm6lj958mSMHTvW/js9PR0xMTG6lqGEM99mSUabhWvKk+XQePNGlL+e7ERpBQeeciSlGAHAxmmIMl4cWBtAnabVsh39yjzqg5GiZzwoJzsKsefpFyh/zZfN1eXtEBvLhWct7Kxsiq4blYkieIQRfmuUEyoh+xi8UDnyVoSPSs+6oGoQJlPef9Lbk/jKtJpm5SgmJsa5WBFOMm7cOAwZMkQ2TaVKlbB582bs2bMHgYH8hq1Ro0YYMGAAli5diqioKCQn8zt/2++oKPGuLjAw0CFPT1KNuYqW5pOK6Ww7X6ueNlk/FR1unXa+/RbMo3lsusZFvFJqvabVMt0/JSyLU3vk5SGcmnF6tZqIcqRX+ybcy9CVTkFoqXKrhYUFcrNxMmiYTrnJTKt5o+VIF3xDIdCE0+/KN56FZuXok08+waRJk/DVV1+hQoUKBojEp1SpUihVqpRius8++wz/93//Z/9948YNdOrUCStXrkTTpk0BAM2bN8fUqVORnZ0Nf3/rZonx8fGoWrWq6JSat8BtU38M+D/phBxyYUIJpGEcliknBoBbp5XTaME36r8D3rmaRSflKMNROfKoEisSdVw9EtO4WhtsMcuR1uCaUggVOJeUo1z530bz8I5yGl3wRuXIG9sEwFCHbGfKV4u3Pk4BmpWjfv364eHDh4iLi0NISIhdybCRkiKzysNAYmP5G2aGhoYCAOLi4uxhBfr37493330Xw4YNw8SJE3HixAl8+umnmDdvntvldYYI3ENx5r6qtBaY8LH/QrRljhoslQReMQJ0Js6RF365PMuRC52iyL5pDFjPbA0CyDs9O4vWeif2PPVSPISdlbOr1QBHmVgLkHwSKBLpfJ5q0flbtu89eF9jBP5rMlM1RsKyQK7C9jrKmegiCj9LD7exRpWvtOLaTThlOfJVwsPDsWHDBowaNQoNGzZEyZIl8c4773j9Mn5bNUkIekU2HZdc1oT6JgmHOTcQ+N8uBKGNckK1bHpP+zWebjx0g3MfrsTKyTUgzo6nkHTI1vjOE0UGD3pZju5eBspq2QlRBqFM/x2wOr76BYmn1xV9v6POV+cCv10HgsJFipIp69v2usoBwHFDajEy04D3SyinczvGBYFUheqQA0JURPI3uxRlSBc0SzB48GAj5NCdChUqiPpG1alTBzt2OLm7uw+RC5NHrSD+Kecw3/8zj5XvLF5nObKFE7CxbpLzeYl8Dwznvx7l5hlgQVMNF+gk88nfHY/t0qne/vIiUL2HPnkJLUdn82K45WTok78cRnS6x1YCUXXECtO/LCm2zQG2qHNTcB0DvjFPD/7Ob3TuOiWrkCXHd5Sj9PR0e8BHpTg/3hgY0tdxZqm1RXsIK91pbz7iWQG2zgDaTtR0idcpR/cVlpVrYZNESIp/1+lXhrOs6KctvWSsTh06jAubXM/DhshUplMIHbLdagVkgUNL9M9WuN0E4N4O322KkUFk67vlk9cgFybDjahSjooVK4bExERERkYiIiJCtLNmWRYMwyA3182OgoQonrYceQ1icWxkKCgTcWqJYlJcMI/rSJpW/yMNkcw9yXqZWF5aEE6r5bgpAClgVVi2zlRO50y+hQW3ObT7AiosR16AKuVo8+bNKF68OABgy5YthgpEOOKMb5o3WI68AndMO/gw3wR87GkR8tDYUeoVBNJojum0Abcwurm7LUdGILYQwFvfo6vIbUejF17gxKwKNXsXegGqlKM2bayOtTk5Odi2bRtefPFFj24sSyhjcfsibR/5MBUobNY2rcFCDUOvffQKaufqUcuRQfmaaACnKz5T932jjdVUO/38/DBnzhzk5HiH2auw4ExVyiXLkWaK4FGhU468B62WI6n67SsdhEYcfI7cqBwZ9kzFvrUC+v4In0OzS3i7du2wbds2twSAJPKpy5zXlN5qOaKOXgt6RQAm3IHEtJrPjJ41IlytlvPIfWUb9UwL07QakY/itJp31AHNylGXLl0wadIkHD9+HA0bNkSRIkV453v00GnpKpEPw+CPwHc0XWIhh2wrXvKhEQq4+p5yHgErBwIVW+sjj7fhDp8VSYxSjshyVDhR6pe8ow5oVo5GjhwJAJg7d67DOVqt5j00MGmzNBGEZ3HRIfvGEes/b1h5V9Bw5wCDBjPOU1Acst29NY4Emh1TLBaL5D9SjIyBoQbDRej5FTjc6nNT2KHvxycoKP3EwlaelgCAE8oR4X7sexF5MfVzPLSHmxK+MpoitKFXcEVCGbcurS4gHbwnuH7Q0xKoRKFNfnDTKxQ9zdNq770nv8fVO+9o840hlDHptddTYYRl4StLRwnCKzEqVphYB3hhszFlEd6DmgFrbjbgF2C8LDJoVo5+/52/F1F2djYuXboEPz8/xMXFkXJkACYLTSG4hudHIQRBqGD9FE9LQHgDORm+pxwdOeK4X1Z6ejqGDBmC3r176yIUweeJQ695WgSCIAh9oSnvQoqK935xC1C8EhBV23hxJNDF5ygsLAzvvvsu3n77bT2yIwRE3j3saREIgiD0xQv8Sgg3c/eKOqV41SBgzVjj5ZFBN4fstLQ0pKWRkyRBEAShAo/GbiI8wsoB8BUfUM3Tap999hnvN8uySExMxLJly9ClSxfdBCOspDzIQnFPC+HT0OjUq7HkAiazp6UgCMIdJJ0AYpurS+vhaVfNytG8efN4v00mE0qVKoXBgwdj8uTJuglGWBny3X5QWDsXINO9d3NkGdBwiKelIAjCLbAeV3rUolk5unTpkhFyEBIc+y8NCPK0FD7MwcVA2nVPS0FIcXUfKUcEUahQqxz5mOWIIHyKI8s8LQEhC1n2CKJQYVLp6uwL02p9+vRRneFvv/3mtDAEQRQyaNqTIAoX95I9LYEqVKlw4eHh9n9hYWHYtGkTDh7MD1V+6NAhbNq0CeHh4YYJShBEQYSUI4IoVNw+qzKhD1iOvvvuO/vfEydORN++fbFw4UKYzdZVJrm5uRg5ciTCwsKMkZIgiIIJWY4IghAj+4FHi9fsc7R48WLs3LnTrhgBgNlsxtixY9GiRQvMmTNHVwEJgijAHF8FWLI9LQVBEN5Gomc3M9ccBDInJwdnzpxxOH7mzBlYLN6/ezxBEF7Gyd+V0xAEQbgRzZajoUOHYtiwYbhw4QKaNGkCANi3bx9mzZqFoUOH6i4gQRAEQRCEO9GsHH300UeIiorCxx9/jMTERABAmTJlMH78eIwbN053AQmCIAiCINyJZuXIZDJhwoQJmDBhAtLT0wGAHLENJBz3PS0CQRAEQRQqXAoCSUqR8ewIHO1pEQiCIAiiUKHZIZtwL2HMI0+LQBAEQRCFClKOCIIgCIIgOJBy5MXcTM/wtAgEQRAEUejQRTlKTU3VIxtCwGs/HvG0CARBEARR6NCsHH344YdYuXKl/Xffvn1RokQJlC1bFkePejaiZUFj/+UUT4tAEARBEIUOzcrRwoULERMTAwCIj49HfHw8/vnnH3Tp0gXjx4/XXUCCIAiCIAh3onkpf1JSkl05WrNmDfr27YuOHTuiQoUKaNq0qe4CEgRBEARBuBPNlqNixYrh2rVrAIB169ahffv2AACWZZGbm6uvdARBEARBEG5Gs+WoT58+6N+/P6pUqYI7d+6gS5cuAIAjR46gcuXKugtIEARBEAThTjQrR/PmzUOFChVw7do1zJ49G6GhoQCAxMREjBw5UncBCYIgCIIg3Ilm5cjf3x//+9//HI6PGTNGF4EIgiAIgiA8iSrl6M8//1SdYY8ePZwWhiAIgiAIwtOoUo569erF+80wDFiW5f22QU7ZPkCRSODBTU9LQRAEQRBeiarVahaLxf5vw4YNqFevHv755x+kpqYiNTUVa9euRYMGDbBu3Tqj5SX04NnvPC0BQXg39QZ4WgKCIDyIZp+jN998EwsXLkSrVq3sxzp16oSQkBC8/PLLOH36tK4CEgYQUtLTEhCEdxMR62kJCILwIJrjHF24cAEREREOx8PDw3H58mUdRCIMhzMNShCEGPSNEERhRrNy1LhxY4wdOxbJycn2Y8nJyRg/fjyaNGmiq3CEUVDDryuM2dMSEHrD6LInd+HCpHkigiC8Fs0twLfffovExETExsaicuXKqFy5MmJjY3H9+nV8++23RshI6A1ZjvSFOtKCB30i2jH5e1oCgtANzap+lSpVcOzYMcTHx+PMmTMAgOrVq6N9+/a8VWuEN0PvSVeo3hc8SOHVDlmOiAKEptqcnZ2N4OBgJCQkoGPHjujYsaNRchFG4kpnHh4DpF3TTxYAGLgaWNZL3zzdCilHBY6cTE9L4HuYaHqZKDhoGh75+/sjNjaWYhkVZtq9rZymzURtecY94Zws3gJZjgoed87rn2eJAr73pJmm1YiCg2bb8dSpUzFlyhSkpKQYIQ/hDlzpzNVcG1ra+fx9EXLI9n6CwrWlz7wPRNbUWYgCrkTTtBpRgNCsHM2fPx/bt29HdHQ0qlatigYNGvD+EW6k0wwnL9TQSNcfqP3awtZIkn+Kd1Czj/Q5VvqUKNkPgWHrXRLHgfovWP9fNFrffL0FGiQQBQjNvZhwKxHCg5R8zLnrtFiOomoDJaoAd845ngspCTy87Xi80ClHRlsEGGjv3QshRUrJn+/3A7DyBXV55WQAgUVdl4lLi9eB6PpAWFlgfkN98/YGTDRIIAoOmnuxadOmGSEH4RTOdsoarmNM4HXMPEVAosMudL4HBXy6xFdQcgiu/pT6vLIfuSaLGCYzUKkNcP+W/nl7A2Q5IgoQpOoXRrRYOuTSshLKUWFbtUK6kXeg5/RmWFn98hJSUL+PgnpfRKFEc2uSm5uLjz76CE2aNEFUVBSKFy/O+0f4Alp6cyd6/kI3reZCpxzt43565Rp7WgKVqJyW9A8BavYGus8zTpSC6qNGliPfoFR1T0vgE2j+St99913MnTsX/fr1Q1paGsaOHYs+ffrAZDJh+vTpBohI6I4my5FcFZGyHJFypC8u+Bs9uxSo9Yx+ogjxCzIub6246vsV0wwYuQd4dgkQbqTlqIB+H2Q58g1MZmBqMjDoT09L4tVobtWXL1+Ob775BuPGjYOfnx+ef/55LFq0CO+88w727t1rhIyE7mhUjp6YYv27/gvAo7v557h/O5t/fZUOst6MK8qR0c7cNXsB/sHGluE1uPgsh60HilXQRRJZCqoSEVBE/rwn4jzV7O3+Mr0dxgT4B1n93whJNLfqSUlJqF27NgAgNDQUaWlpAIDu3bvj77//1le6Qs5jjIZI1MXjgModgPbvKqfVajmq9TQw9gzQY766a1iLunSV2wM9v1Avi9fiSqes5loGqP2sC2UQXkVBnX7yCwJe3qr9uqBwoGJrbdcEFwfqDVBO98x32uUxCm8JAiqmnFeh3S6EaFaOypUrh8TERABAXFwcNmzYAAA4cOAAAgMD9ZWukLMhUEOkab9A4IVfgFZvqkjsxLRaWBn1SpVa5aig+F4YHVQTAHp/7XwZRkLRwbVTUC1HjMkaqkArfsHA4L+ARi+qv2bcGaDNBBUyeVH9DCnhaQmsiLW7j49zvxxejubeqXfv3ti0aRMA4PXXX8fbb7+NKlWqYNCgQXjxRQ2Vm/AcuvkcSaBWOZJa7eZruKTkqXkXrGMMmdjmLpRJeJSCMigQ4qzSZ28vtC4U8SLFRw2a2jsD703Uculjz9INaPYMnDVrlv3vfv36oXz58ti9ezeqVKmCp57SEEeE8CA6LeWXQq1yVFDwSGen5b0YqYRSo6oZb7Jm6Imz3wGbt1en1hAjPvccNXyHDGPc4FFMifW5Z2k8Li+baNasGZo1a6aHLIRLGKTwGGk5KjAYPa3mxQ2XmPzt3gY2v+9+WeQoKFZKbyY0yrnrLLaNzLXWcy/+LlyGLEeeRrNyFBsbi7Zt26JNmzZo27Yt4uLijJCLMBQXlCOlzvyJqRo6ogLSYbk06nLyWq8Z6XmJHEVKAcERnpai8FKmHtBBxWIQMWyDKU112gctR1oUdIYxrnkU2+bF156lG9BsFpgxYwaCgoLw4YcfokqVKoiJicELL7yAb775BufOiey/5Wb+/vtvNG3aFMHBwShWrJjDXnBXr15Ft27dEBISgsjISIwfPx45OTmeEdYVxp7m/9Zi4XElQrbSB978NQfL0d2wasBjndWX6WsY7pDta0qkCnn13vH+uR/hNYqaFmJbFAwfpBd+A4qUdO5ae5tClqN8DLy3BoPVl1esonFyeDmaLUcvvPACXnjBGpsmMTER27Ztw5o1azBy5EhYLBbk5uYq5GAcv/76K4YPH44ZM2agXbt2yMnJwYkTJ+znc3Nz0a1bN0RFRWH37t1ITEzEoEGD4O/vjxkznN3h3gMUjQbCooGbp/KPafqWXLAcKSlHDCMyrcaIL2MtKFMdhjtkezFiyp2a11qjJ3DzpJ6CqEtWNBq4d0PHcl2k71LrsvT381YyRZQHHtwGsh/kpzEHALlZnpFPLa4MEFgn+gyGKRhKpRRGWnJqPZ3/d5VOQPp1oExdCTk0PGP/EOuqacn4d76FUz5HDx8+xM6dO7F161Zs2bIFR44cQa1atdC2bVudxVNPTk4ORo8ejTlz5mDYsGH24zVq1LD/vWHDBpw6dQobN25E6dKlUa9ePbz//vuYOHEipk+fjoCAAE+Irh1XPxyXfI6Uej5H5chcgNswK0YrOGL5e4tSJSaHNyq9eTINWQPs+gR4rAvw0/OeE2fSVSDzPhAayT/e7FXg7hVg35f5x/yCrEutt850r4xakFqpFhQBZKTKX2vJs9xrbdd8bSqoVh/g+kF1aS0GGRkCw/jPrf9K6/91e5Y+9k5k0NxttWjRAiVKlMCkSZOQkZGBSZMmITExEUeOHMG8eQbuR6TA4cOHcf36dZhMJtSvXx9lypRBly5deJajPXv2oHbt2ihdurT9WKdOnZCeno6TJ8VHsZmZmUhPT+f98xp4fZBRlVI4rabgbC1iOSoa5OHtEko+Zmz+UqOr5q8BLUcrXOsDPkevHTJADp0VKLVylIgDenwOFK+kb/laCQpXv0WJOcBqafNmxL6B8i35VgopnF7K72NUbA28uidvClgBZ6xpNrQEdNR11Z8P+oHJoFk5OnPmDIoUKYJq1aqhWrVqqF69OooVK2aEbJq4ePEiAGD69Ol46623sGbNGhQrVgxt27ZFSkoKAGt0b65iBMD+OykpSTTfmTNnIjw83P4vJibGwLtwE4auVnNUjhjJftBNFobQ0spptNLyzfy/pZ5npw+ADu8pZOTlPkfBxYGSGiP7qpkuNWJKtaA0zML78Kb966RQFfVb4v04ayXxxWn50jWAgFBjy9AzAntB+aacQLNydOfOHWzevBnNmjXD+vXr0bJlS5QtWxb9+/fHN998o7uAkyZNAsMwsv/OnDkDi8XaIU+dOhVPP/00GjZsiO+++w4Mw+Dnn392uvzJkycjLS3N/u/aNQ1bengt7vY5KoCYuVOwboiQ7SkUlWNnp9U83LGptjRVMVYOB0Tk8vOB6X6xeqJ11aorC0V8gjyZjfaV8mj764vvRRzN8x0Mw6BOnTqoU6cOXn/9dRw6dAjz58/H8uXLsXLlSgwfPlxXAceNG4chQ4bIpqlUqZJ9SxOuj1FgYCAqVaqEq1evAgCioqKwf/9+3rXJycn2c2IEBgb6xrYoRjUsDsqR0ocnohxJFufDHxK3wyrQjqEG3RvLWldqXd2tU4YG1aXBfwFzqxmTt1p8wnKkRz3R+g590HIEuEGxk3suPtzmuhnNytHhw4exdetWbN26FTt37sS9e/dQu3ZtvP7662jTRv9dfkuVKoVSpUoppmvYsCECAwNx9uxZtGrVCgCQnZ2Ny5cvo3z58gCA5s2b44MPPsDNmzcRGWl1hIyPj0dYWBhPqfJNDJqv1+qQLWY58vS0mhHmdzNHYXalY/D2IJBKW0KIrlZTaTka8DMwU6Xfjas8McW568LK6CuHEmLPs4QPxJITrScav7uCbjmyy2yw7J6abmQY31DkVaJZOWrSpAnq16+PNm3aYPjw4WjdujXCw8ONkE0TYWFhGDFiBKZNm4aYmBiUL18ec+bMAQA8+6x1R/OOHTuiRo0aGDhwIGbPno2kpCS89dZbGDVqlG9Yh4Q4+425ZDnifHh+wUDOI8drvM0XIFRZudaMH1c58sBI0F2blxo1rcZagEAdfS8YKVlgDRDZbKTYBV6KQLZuc4GHdzwjilqk6gn329D7O1FqZ7rMtv4/rKx1ubq34IuKnVr6LQNWDgTaTgL+fM3T0riE5iFvSkoKDhw4gI8++ghPPfWUVyhGNubMmYPnnnsOAwcOROPGjXHlyhVs3rzZ7jBuNpuxZs0amM1mNG/eHC+88AIGDRqE995Tcpr1AbgfXOlaSonz/wyPVZ0UAN8q1OpN66obLiazyEoLiUbMXUpU2Yb65xnTNP9vT8Q56jLHhTI1oHRvWhr6FzdwL3RKHKcoUdm3OiSurK8ddFzurzcDfnU9DykLIu8bV3gHer6j4ZuBpq9Y/x613/F8ucb6laUaN/kcyQ1OND9ijReUbQCMPQnUfkZrQV6H5rcUFhaG1NRULFq0CJMnT7avBLMtpfck/v7++Oijj5CcnIz09HTEx8ejZk1+JN7y5ctj7dq1ePjwIW7duoWPPvoIfn4eXmquGYW4Ny+ux4857WQu56QVCyXPSyszreYXCLSf7niNmF+SO61JbScDfZfxj+m5QiSkBBBdj3PAAIfsSm3lryulIjyBSaFev5EADF0nn8apjYcl3nV0fe15qUYg5+ijBpbFIbaFzhkK7qOkwQ7hT38LVGnvWh5jVAbz1F1BlVMCOJZVMQtlYFGdZdGC0dNqnnLIZiT+9k00awXHjh3Dk08+iYiICFy+fBnDhw9H8eLF8dtvv+Hq1av4/vvvjZCT4KGgaASG4igbh/7YnH+s7WROEDkNlVhuWk2qE/TktNqES0BIcWPLKC/oENW2AyWrArfPCg5KXBxcTDmNEgMUVmkWr2j9J4fismAN02q8ztHgOsKziIrIqFdH7UsWKaMILydxgtX4fDQ+S943IsxKIS9PtFE2mYyuMx5rfznlFoDvQrPlaOzYsRg6dCjOnTuHoKB856uuXbti+/btugpHKMCzWDOSp1C2EX+PHC1+AHKWI6kRiuoAZgZ8xEYrRmIY4ZDNa+CcfE7RDZy7jouWabUy9az/ryVlUndjg+mrjbOvyi2FkdNqAUU0i+MVeHJazW34fj3WbDk6cOAAvvrqK4fjZcuWlQykSOiNwrQaABYCBYjnwMs9p3FaTdhpi41SRJUmb/hg9UL4/NU2BFqegZc8Ly0O2S9tBB6lSjvAG9nxC/M20hGYX5CBefs43rYwg4cnZCvgq9WEfY6Po1mFDQwMFN1C499//1W15J5Qia4VnAHM/pyfOilHUiKqnfP26sZTAwV541kt92b2V1gZ6Kl7daNS5u20meTe8jQpqXo+Sy98L/ZpNaML0jHOkdP12wufv0Y0t+o9evTAe++9h+zsbAAAwzC4evUqJk6ciKefVrGPDqEOvRUHyYjOWhssFdM9gWGCS9ykBEXWVE5jBIZPqwnSVO7gfHlacSbOkUeQkcNrZFSBK7I+9alymicmAxEKK1R1w4Or1ZRwR5v0+mGJBQ8G3WdAnpN5ZRcd7PXAl745CTS36h9//DHu37+PyMhIPHr0CG3atEHlypVRtGhRfPDBB0bIWEhR8fHy6h8/vcO0Gs9yZBL/2z/EcWSp6JAtImf9gUC17nKS68+T7wADf9cnL79gIO5JoOEQoGYfx/NyUziaUXOt4Bm7s+HR0z/CaLm9uUFWu3Q8ur7z99FwCP/38z/xfwfn+eMNWetc/kZRb0DeH178/pyhRBwQEMI5YPBS/jEnrBvb6uFr6Aw1enB++P671PyWwsPDER8fjzVr1uCzzz7Da6+9hrVr12Lbtm0oUsRHHeS8ERdHNiwrqJxcy5GUqXvQn9aRJRfZCNkSMvoHAc8t5x9rNtIaVZrXgMvco9yybzHFq+kIoKhOG8xGxAIDf7OOxIuVV06vtrETe6dS+RcpKVeguvL0UBb0jHPkrDzlmgD+Cm2L5rx1arzVKj3C0BJC3jgCDPrDmJhcQiJ03Dw7TGqlGqz1XWlabchaoPsn0ucBoFR17XJ5i6Is9v1IySa1MlTt/QdHWDe2lQ1xYMBzYUxAj8/zg24aVY6b0aQcZWdnw8/PDydOnEDLli0xcuRITJgwAe3be4EZr8DhonIkPCA1rSZlRZI6xrMcWawB9tQQEQNMua7O9N/kFaDfD9Ln5c5J0XmW9mskcdYhW4R2b/N/Fy0DPNbZ8TivOJnP1rZiDMh/V65UpZ5fuHCxDFqU/6JRwKQrjgFHHfBAgxzXDui3XDmd0jYkxSspx7byRjRFORd5PxVacvYplHh/A34G3jyunL3NMuZViCmHEvcpNYXd+0v9xDEi+GWRUkCDQfy6IKUcNRwqfnzQH0BF/bcfcwVNypG/vz9iY2ORm6t2qTbhNFotR0rp1ViOxCq08BjX2ZpltVVo7tSe7XohgeFA19kysVMk5FTqGBsMBJ5ZrCiiU6geJYncb3AE/3e17kD/lYKQBMJpPJnPVs8RW4vXgTJ1FBK5SSER1h130ucb+fPV3TGFrIcV0IB3JdvuCKeDnSzDZHb0YxTDG60Vom2qxPcrGbRV433JvZNeWhUtNWWrSFP3eaDjB/puGWQwmqfVpk6diilTptgjYxNG4arlSFBh/YPz/+Z9sCJWpAaDHY+JyqU2yJsblrCrkaOITtswOCiMRjt3Oulz5HJn4YWdjSSMzHSFCqVfjjp9gce6OCeWUxj13L38fUq+E5l3K0wn+rcYblokwms/bT5HUpYjKeVIJ1nrD1SYrjeIFzcAvRcCLV7zqRXKmuMczZ8/H+fPn0d0dDTKly/v4Gd0+PBh3YQrzGTlWBAgdVJFY/9ymzhgD+dAqWpA1W6OUWV5W4nkmXWrdgEOLxUvS02E7MKG2o5W7W71LuHGDjCqtgsKmIb7VFuGR7eEKKToGgFbSrk1qbhWqyzuQoOV2+jNpC05xuavBrk20Mven2blqFevXgaIQQiZ+OtRzFNKxDPi8Ctd9SiBGZphgOd/FMmEUyFDSuQdkvFD4sUw8iblyIMfll8QULM3kHkfOB8vk1Cn5+WJRmTcWeDjqvxjL20CfnvZ+LK50d1tNH4JOLAo/zfDAEEREhl4V6OriDveb4XHgWv7jF32LewIhffFdeCVQ/Xz8ML3LOa2IDWtJuaQHVYW2u9Lop3JzdKYjwFI7qrgTX2JFc3K0bRp04yQgxCw9ngi5gUpp5NEy9TLgF+BzHQgLNp2kHNebvsQAyq0kR+Jbp2OyFL+Z5dY/56u5DSsgKr7l7kPXTtWjixFo/ingotZNx42uiMv3xJoPd7xeOdZgMkf2MfxobDXXwGGymhAfW0wGNj1KVClo2v5yNWlwX9ZO0u/QNfKqN5DOY0dznuYcsNx+w9Xp9W0vGe3dcYapnSF02qjj1mdnW//q6IYjmIlNpgAgKpdlfNxBk3PXS5AsHcpt0Zv8kI4CaM5zpGmk/x0VdoDtTjxfOQsR42G5f/t6u7Pog2UgT5HeuOX58dV6Ql16fXaLdssOeHKX7HjHyKdzuOofF9dPxJ34jT7A01f4edXrrE1VES7t7SX9coOdfJIoUlJUKBEHDD5P6D/Kv3yBBytGFzF6OVt2vMrEimuuNphgVCJ8Bqi+6LJTaspwfCv95YpGlHZJWQrEcf/Xay8IE6SgIG/W639RaOBF9fzr+P6jb64HhjyN1DLiSDNejxHntKnZlNq74CUIy9FlXLEQ0enXZ5fo6CKOBMjRWqUJla2bWpPMy5+XC3f1FBUXlmvH7SuZGr2qrrrXFWOOv6fNQZT++nSafyDgDcSrKNOV1Z4BYZZY1MpYrDPEbeOVO1m/X/xvE5EWDcZxhoqQrbDliCqtno5+Ces/3t2iTVwqI23bmqXgUtgUflvuOkI1/IXEl1P+zV1+nKW4UvQ/DWgdl9rnCfFTa41ONSLpfHCDlb1YoAavbSHzYhrB0y4CIw7DcQIluhXeDz/75KPARVauf/5NB4OVOnEj1kn1wZ62fsj5chLcV81UVhqKjtqc9E0zR1RDPrDGuxPGNXXXTgTtTa8nLWDUKuEuOqQ3eJ1a7wXWQWVAYpXVBe8UoqgCGDiZelpKl5xBtdU7nvpOgfoPg8Y+o9j2XJyuKPRNZn5z9wvEHh1tzWo46A/Xc/fwV/nQ/dEQi5WQfx4ZA2g1Vj5a1nWavl4+pu86MlOOmQ7Q4kq2q/pMocTrVsEucC0AFC2kfx5OZ+jvkvVfW+q4bQjatu2wX9Z22CpaTmtdPsIGLAKMHHK90LfIilUPTWxjWYJY9FsOSqq44cltvxUDFvU4udWWJ2SbX43auEqR5XaAi/F50V4VUFMU/5vPTtAozpTH2oYFFfO2O9Fp2dVfyDf8mKDWxcDQ4FGL+ZHQveKrU0479QiiP9WuiYwfDNQyaDgdqJTU3I4cY9vJDjGBwsrC4zcAxTRaOV12nKkdlqNg5JFS4zQSKDXAqB0LfHz1brJX1+sgnWhAE8sPVfzaYDb1qiVoWLrvDZY6x6VOvgceWHbqKp1KVasGG7etJqJ27Vrh9TUVCNlIuCEctTtY0EGaqfVFKJii+XT7WOr2bZ53rRLta5WB8uavdWVaUMyrocK6vRz/lqXcbIRUzOt5mojIfreXZyi9Shyz9q7zPBg3Rwc1x0WMYZx9FVRXUf1XJ2p8706850JlV81aIlzpCtOWI5E0fu5FzCH7NDQUNy5cwcAsHXrVmRnZxsqFKGymvhxlrM5bE/ggs8RV2kR+7AavwQMWcOPK+NMjA6X4noIGzfv+rDEKWBxjgDnG3qHzkliE2PZ6TKVFk63PRNfqIN64GwddXZaTaXPUUSsVoG04YxypGX7EMOUJm+ql9408JJH1dC9ffv2eOKJJ1C9unUDvN69eyMgQNxsuXnzZv2kK8TIW47yKntsC+sqmZKPiSThfBBaR0kmjg+NLlMXEuW7YjkS4mrD4o4NVIWjJq7TJCGOauVII047/svQdhJwfiN/RaehiDybXguB1RLO2mrrbfE4IOUCEO6isqEU50iIpG5kUrfyss83wLpJVt88ZeFUpBGgGERRpXLvDssRq5flSGcKWhDIH374AUuXLsWFCxewbds21KxZEyEh3rxMuJBgMgH9pHb7VlnRwsqK5Mux6Bj5YRkdEdbbEDYMYhvoGjKtphFNG6Dq1KCxEnmp3UdOq0N2cIR1xde+hSoFlID7vsLLAWNPe7aRr/c8Xzmq2Aa4ZFumr1KuUlWtzrlSCqS7/UMYhu/UK57I6hD//AoXClK4L6emTRmJv5XScg87U5+c8DlyFq+ML+U6qpSj4OBgjBhh/eAOHjyIDz/8EBEREUbKRRhtfnzhV+Dgd9ZVL0LMOluOpD4IVyxHDg23xgaAMVmXmu7/Suyk0sXayrLDeQ6hUY6bznqa5q9Z915qOET8/Mh9wAKbI3zevei2fYjUtJqccqS2bkrIWL6F68qRQ1EGdUTOrmQa+Dvwnsbd6lkWCBcZNGlG69Q353xQOJCRJn6dv1ZHdBGM8DkS86dRinMU92R+jDmpuuOMrN5qOZLs11h41/SfE0v5t2zZYleMWJYF60OaoC/hcjVRaqQrtweeW25doSGEN61mYIV1RTmq3oPvlK1VzrduAl1Vbl+gF1LOiFU6cRPl/2kLoBfTTCFjLaNTGcJjgFZjHPffsxFZTaFsI1DpS2S4ud7DDXdgUZHglkqwzllnFRcOGBSotV5/6/+rdLKugLVfl9dNDfkbKFPXatXSmrcalFZgcpUjMf8mUeVIwbr59CKg/guqRXQOjc+m/kDr/40IFaFXIFw34JRK+f3336N27doIDg5GcHAw6tSpg2XLpKZ3CGfQHgTSMQenUXLI1iyKhCyuTKuZzECfr617fo2/oL1xdCVAorNwGwauvM8tF0//4jqgxRvWGChylKkrnq+RuDwmEsgpGShU5bSaDzW6ThNRgf/bsJATCs9S7YDYIZmCvGHRwNQkoP9KfmRt231WaAW8sh0o11Bd+XI48+y4PkcjdloHEj0X5B8TfW4KPkeMioGNy9NqGtvwqp2B1w5a2x9V6LCU3wvRPHSfO3cu3n77bbz22mto2bIlAGDnzp0YMWIEbt++jTFjxuguZGHEZeXIlYbTrLNyZMS0mg3hnl/uQC+HbBtcRY37rIpXAjq+b5w8eqBn2VW7AhcECzrUKkdqFjAYgjdbzgX3rfpdefCe/INFDqqRW+M7dmqqimM5CgrPj1T/R15IE4vStJobV6s5E+eIS8kq6q/Vy+fIFx2yuXz++ef48ssvMWjQIPuxHj16oGbNmpg+fTopRzrh0Wqit+VICrFdqAsyvHahoCzb1dHnqNGL1qm9RynA6rwtWdSuVqPpfRGcfCaKz9Id+x+60aFYrEwxlFarKU2riR4z6t681OdI0nLkXYoR4MS0WmJiIlq0aOFwvEWLFkhMTNRFKMLD02o8pUWPSiu4l1ZjrLtNPz5Oh7z1Qst96mw50hOljiSsLPDCb9atLdxRnlrYPP+Yqp35fnCyDbtayxGhDaVVW2541lodijXXQ5F7UNpyxBmHbNG2ws1xjrzKIlOAHbIrV66MVascd4teuXIlqlRxYj8bQhR/KMXUUMCVD4LXGBnQELafDvzvnEjgyoIO51mWrKycRgvlW1n/L7XSjEvlJ53YIkCIgpxx7ZzPWm1wR7WWIyM7B49arLTel8r0ut2TK4FaudcaMK3G5eVtQL/lQJk68ulskcKLVxI/L7bU39ktU1zFHfWy1jPW/7edrP4aH7Lwap5We/fdd9GvXz9s377d7nO0a9cubNq0SVRpIpzjPf8lLubgikM2x3LkVFRYIR4KhCakqIeVMdYCvLQZ2P810H6aeJoy9ZzLe9AfwP1k8SXYhjZIEu/x+Z+AW2eB5BPWKbKqgn2pRCNki+Speim/mxvd8BggIBQo39K95RpJzT7Ayd+AVm8qJFR41m0mAdtmOYYJ0fLNc79VPfxeHusC/PuP+LnoetZ/SlR+Enh1j/SmzmJtpVgddff2IUbR43PrQFd2I2wBUtbzEpW9zMLlhHL09NNPY9++fZg3bx5Wr14NAKhevTr279+P+vXr6y1foaWj+ZDnCudGo/UL1CFDLxktlIgD+ixS3jDTqNEey1pX2pQTia306m7g0g6g4VDn8jb76RSbRiVKCpdfoHUkXqZO3k7fFdTn7UxwR1l5DGh0+y61LnX2sgbdJZ5ZDHT9SPuGskKemGydOvcPEpzQ8KyCwoDXDlk3kJV7xrX7AsdXAa3GyufX7wfgfc59OTtgkNsYWylIpFioAK1yhJUF0q8rp9NtQKTw/WlRjADr1lOnVguCk8L4rV+cwKnlQg0bNsQPP4hE9yXcgyorswuNdkAI8OwS60jIlUCFAUWBrHvetU1GnWc9V7acz1Hpmi5MdXmwg271JpDwA9BgkHQaySlEDqyU5ciFabUavawNsartJBTw1DYQ7oRh1ClGajpeB8XICdTUm15fWt9v6Vry6cwuroxtKrElCxex5yK2olL1KksRgourU468lYqPW6PIh5bOD04qFVfNw+i4uRXhXbjYcNfs7boIr+4EzvwNNBjsel5ehRc7ZLubklWsATVdtjBKrE5yxSH72SXAo7tAiMYI0WrwiDFU415lQnRT5tyxWk0lZj9lXyFRNN5DpSdUZKnWIZt7jVEVyR0V1Mn3KRnt3bsGG160xo9QTbXuymmiahsvhxLFKgDNRwGBoZ6WRBm3bAZZAJUjQKepVy4qlzorWY4YxhjFyBvp8J6KRN7V+RQ4lHyOlCJwS8KI/ukRpJzR9cDLLLFkOfIlnv4WyM1WZ9Vxp/9JYUO3/cR8GZ3vRSponS5BIA3Au9pxoOVo95XlQyuOJJFd3ehsnkpxjkSm1VQ9Sxf3VvMZvOujIuXIlwiNBCq29rQUhQslXwYtFFTLke44sVrN1c05CQ140bSaN+FqnCNVuDnKucM7U+kPWACgaTWfomBXRq+kZh+g21zrnk7eiicaKd0VC6m91VSuVitQVjkpXHzPHu/MPF2+K6ioX6JxjpQUeDX11onnptf32WaCPvnIknd/wvoZ7dnV75otRxkZGfj888+xZcsW3Lx5ExbBfjKHDx/WTTiC8DgmE9B4mOCglzXyPmkBURm0Ue3WBz75DLSiMahiZHXr/0tUBu6cB6r3MEYMtXhcOTMYtduHhJQAiscBYK2rz7Tg7mdYoycw9gwwt5pI+TrLwt1j8sl3gHov6Ju/RjQrR8OGDcOGDRvwzDPPoEmTJmAKeoX3JsjXhRAjoIinJXAdyaX8ao3b7q7jXtzuvXEEeHAn33l26Drg4hb9lKOCTscPgO97AE1etgZsBaDqfYttPCsW08hkAl47kP+3t8PbycDAet/hPeBGgjVsQjMVoRMMRrNytGbNGqxdu9YeHZsgCh1a24cipYAHt4AoZ5Ycy9D7K2DnJ8BTnyok5CgORaP0lUE3pDYadfeWF2rx4gFH8Ur8VUWhpYA6fXUswGnTkY4yuIrMPVRqA0y5YVXM7cqRTPon3wE2vQd0+wg4/jP/nNSA1qSw6XavhfLnlfBFS2pELDA6wdNS2NGstpYtWxZFixY1QhaC8CAGmotfXA80Hg4896O++dZ9Dhi11xr5Wy1PL9JXBmdRG9Fa9bQaObu7DWc7Xl+aZQgoAtXtwOPjgLduAbHNHM85u8VNkZLq04riBuXIl96nE2hWjj7++GNMnDgRV65cMUIeQpaCXRkLLCXirKNKraH2dYNTb+TilGhq7Ny1lN/Nq3N8CZ/rnHxNXg34BUiccHarEC99Vj5X55xH87Rao0aNkJGRgUqVKiEkJAT+/v688ykpKboJRxDeSeFpIDyDE8qR26cRCnMdKACKqJr6orsioMNzC48BEo+qKMqod2RAvfdShUuzcvT888/j+vXrmDFjBkqXLk0O2e6EnjXhLbja+DrUZSmfI5WEUdBTt+Hsu6//AnBhE1Cmrr7yeDNc3yLJbTNEkPoEOs+yTtU1HKKQgUHKkZGr1bwMzcrR7t27sWfPHtStW4gqOEFwISXVdYQdrNRqNSWGrAUe3FS3SSnhWWr2tu7FV8Ib3pVB8YUcsmCsS+Et2UCgFl9dibKLlAL6LVO+3JcsR16KZp+jatWq4dGjR0bIQhDegSvKzxNvWf/f2h3B0woopR5Tn7ZCS302SVbCKxViT8nkgkN2VG3AP1hfcbydsDLWlVhacMrvjtATzcrRrFmzMG7cOGzduhV37txBeno67x9hJIXsI+nzjfvK0tQAyaRt/T/gzePAE1NcFsltVG5v/X/NPh4UgtPhBhYFJlwCJv/nOXGEPNbZ0xIQhQpX23pareYqmqfVOne2NhJPPvkk7zjLsmAYBrm5IiHUCc3ksgzMTAFwfHSFOn2B5BPALqU4Pl4Ew2gfJXqaAb8AOZmAf5CGi3Sqm0WjgXs3HAMUhmiMHGw0dfsDISWBFf08LUk+nuqcfDGGjhCPOGTrgKcXKHjjMzEIzcrRli1bjJCDEMCCgUMHVIgqpp0npwO1ngZO/g7snOdpaQomDKNRMdKRV3cBN08B5b08qKzJBFTp6GkpvIQCoBxpxV0KYY2ewK2zOnwPvuRz5J39mmblqE2bNkbIQQi4iWKIxh2dcvPOyqcKk8m6uuXUn87nobePQ7nG+ubni+jVWYQUByq00icvoymMgxMxKhaEPsBNDtla6fu99dvy1rpGq9Wk2b5dfnfy1q1bOy0Mkc8hSxVEm11UjrrMAbbNUrG9RAGlw/vA+Y36bWA46gBwbS9Qb4A++bmNQjjSNwJv7bDcQYXHgcs7gLKNgN4ubm3hi7jz3cuW5a1xvwoempWjtm3bOhzjxjoinyMj0fiBNn0ZaDK88DbqLd+w/tOLUo9pW0lFeDkudCCe+KaqdgVCo4DYpjYh3Ff2wN+BtGvyEdYLMj6nbPjStJp3onm12t27d3n/bt68iXXr1qFx48bYsGGDETIWSnSrgoVVMSKIgkZgKDD2FPDsUveXbfYvWIpR5w+t/5cLueHLbWfd5wGTP1D9KePK8OXnowLNlqPw8HCHYx06dEBAQADGjh2LQ4cO6SIYIUIBr4yepQDPpVfrBhxdYQ0gpxu+NpLWGU9ZEpR2cyfUEdvUulms5J5oXoraPiA0Ephyw6rU6lq+vtlZ8/TO9lazciRF6dKlcfbsWb2yIwp750PoR7XuwIvrgZI0JUgQdhQVI+/stFVjiOLn489EA5qVo2PHjvF+syyLxMREzJo1C/Xq1dNLLoIg9IJhgNhmnpaCj8/5cAjwhtGuN8hAFC70jNzd5BVg/1dA++mu5WMQmpWjevXqgWEYsILGrVmzZli8eLFughV2GDHLEWtxvyAEIYavKzeuUrSMpyUgCiUFSCHu8iHQaox1exUvRLNydOnSJd5vk8mEUqVKISjIQ0HkChOFvUMiCE8zfDOQeQ8oGuVpSQij8RrLnLfIAegqC8N4rWIEOKEclS9f3gg5CAHiVZCUI6Kg4KN1uWxDT0tAeAQfra+E06heyr9nzx6sWbOGd+z7779HxYoVERkZiZdffhmZmZm6C0hwKNTTagY3Tl4zSiTcRlw7gDGRwkOI441tgqdl8nT5bkS1cvTee+/h5MmT9t/Hjx/HsGHD0L59e0yaNAl//fUXZs6caYiQhRFxnyMavRDeQgGoi0HhwJREYNhGT0viJIWno/I89KwLG6qVo4SEBDz55JP23z/99BOaNm2Kb775BmPHjsVnn32GVatWGSJkYUPo7M4541Y5vAuDGqcy9az/r/W04UUVCMyB1v+XruVZOfTCP8i6fx9ByFKY214uhadxVN0q3L17F6VLl7b/3rZtG7p06WL/3bhxY1y7dk1f6QopG0/fFK+CNK2mPy9tAsZfBEpWsS4pZUxAt3nGlFUQeGUb0GAw0O8HT0tCEIUQmlZzF6qVo9KlS9tXqmVlZeHw4cNo1iw/dsq9e/fg769zNM5Cyt6Ld2hazV2Y/YAiJax/txpjjZob09izMnkzkdWBHp8BETHOXV+isvX/NfvoJxNBEITOqFaOunbtikmTJmHHjh2YPHkyQkJC8Pjjj9vPHzt2DHFxcYYIqZZ///0XPXv2RMmSJREWFoZWrVphy5YtvDRXr15Ft27dEBISgsjISIwfPx45OTkeklgcf7PUayHlyHDMugWNJ8QYsRN44wgpoHpQqqqnJSAKHYXHcqS6J3j//ffRp08ftGnTBqGhoVi6dCkCAvLDky9evBgdO3Y0REi1dO/eHVWqVMHmzZsRHByMTz75BN27d8eFCxcQFRWF3NxcdOvWDVFRUdi9ezcSExMxaNAg+Pv7Y8aMGR6VnUuAn4ksR0TBxD+4YG1g6knaTAAsOUCNnp6WhHAXhWhay9OoVo5KliyJ7du3Iy0tDaGhoTCb+Rsg/vzzzwgNDdVdQLXcvn0b586dw7fffos6deoAAGbNmoUFCxbgxIkTiIqKwoYNG3Dq1Cls3LgRpUuXRr169fD+++9j4sSJmD59Ok/Z8yQBZokPgJQjgiBsBBQBOn3gaSkIoyGFyCNoXqYRHh7uoBgBQPHixT2qXJQoUQJVq1bF999/jwcPHiAnJwdfffUVIiMj0bChNY7Jnj17ULt2bZ5jeadOnZCens4LU8AlMzMT6enpvH9G4282URBIgiAIwrsGxUzhWdlZYBwsGIbBxo0b0atXLxQtWhQmkwmRkZFYt24dihUrBgBISkriKUYA7L+TkpJE8505cybeffddY4UXIOlz5E0fCUEQRGHBW9peT1uRPF2+G/F6NXDSpElgGEb235kzZ8CyLEaNGoXIyEjs2LED+/fvR69evfDUU08hMTHR6fInT56MtLQ0+z93hCsI8DNB1EpUqJfyEwRBFEIKkULiTXi95WjcuHEYMmSIbJpKlSph8+bNWLNmDe7evYuwsDAAwIIFCxAfH4+lS5di0qRJiIqKwv79+3nXJicnAwCiosQ3kgwMDERgYKDrN6KBAFqtRhAEQQBW3zJvoeRjwLV9npbCLXi9clSqVCmUKlVKMd3Dhw8BACZBtFuTyQSLxWpxad68OT744APcvHkTkZGRAID4+HiEhYWhRo0aOkvuPAxD24cQBEEQAIpVANpOsW534ymGbQT+22+Nc3ZkmefkcCNeP62mlubNm6NYsWIYPHgwjh49in///Rfjx4/HpUuX0K1bNwBAx44dUaNGDQwcOBBHjx7F+vXr8dZbb2HUqFFutw45BU2rEQRBFD7aTgSajfBc+TGNgeajCpVDdoG505IlS2LdunW4f/8+2rVrh0aNGmHnzp34448/ULduXQCA2WzGmjVrYDab0bx5c7zwwgsYNGgQ3nvvPQ9Lz4dhGPtqtfHZL3POkOWIIAiC8BSFx//J66fVtNCoUSOsX79eNk358uWxdu1aN0mkM4V5Wq0w3ztBEAThVgqM5aigIepzRJYjgiAIgjAcUo58icLsc0TLWQmCIDxLIWqHSTnyUmyWI5Y7x1uYp5YK870TBEEQboWUIy+k6L3zeMJ8VOQMKQgEQRAEYTSkHHkhnbbm77LNsmQ5IgiC8AnKt/S0BIROFKjVagRBEAShPyoHpjV7AyY/oExdY8UhDIeUIy+n9WOlgMt5PwqRMxxBEITPwTBAjR6elsJACk8fRNNqXk6pUH+g2UggugFQrbunxSEIgiiEFB6lgLBCliMvh4EF6DzT02IQBEEUYsjfs7BBliNvh5ywCYIgCMKtkHJEEARBEIQyQeGelsBt0LSat0OWI4IgCMIbKFMHaDUGCC/naUkMh5QjgiAIgiDU0X66pyVwCzSt5vWQ5YggCIIg3AkpR4SPQEoiQRAE4R5IOfJ2yOeIIAjCs1A7XOgg5YjwESgIG0EQBOEeSDnyemjEYoWeA0EQHoK2bip0kHLk7ZBOQBAE4VloWq3QQcqR10MfpRUauREEQRDugZQjL4dUI4IgCIJwL6QceTkMqUcEQRAE4VZIOSJ8BFISCYIgCPdAypHXQ0oBQRAEQbgTUo68HdKN8iCHbIIgCMI9kHLk5ZBuZIOeBEEQBOEeSDkiCIIgCILgQMqRl8NQ8DGCIAgPQ+1wYYOUI4IgCIIgCA6kHHk9Fk8LQBAEUcihBSGFDVKOCIIgCEIWmlYrbJBy5O3QN0kQBEEQboWUI8I3CC/naQkIgiCIQoKfpwUglCDTEQCgwWAg5RJQqa2nJSEIgiAKOKQcEb6B2R/o9IGnpSAIgiAKATSt5u1QnCOCIAjPElXH0xIQboYsRwRBEAQhxtjTwMM7QPGKnpaEcDOkHBEEQRCEGGHR1n9EoYOm1bwemlYjCIIgCHdCyhFBEARBEAQHUo68HXLIJgiCIAi3QsoRQRAEQRAEB1KOvB6yHBEEQRCEOyHlyMt4kJnjaREIgiAIolBDypGXMfOf04IjZDkiCIIgCHdCypGXceDSXf4B0o0IgiAIwq2QcuRlMIzwCGlHBEEQBOFOSDnyMkyO2hFBEARBEG6ElCMvw0RvhCAIgiA8CnXFXoaj5Yim1QiCIAjCnZBy5GUwQuWIImQTBEEQhFsh5cjLMJHLEUEQBEF4FFKOvAyaViMIgiAIz0LKkZdBliOCIAiC8CykHHkZQp8jhgxHBEEQBOFWSDnyMshyRBAEQRCehZQjL4N8jgiCIAjCs5By5GVQhGyCIAiC8CykHHkZjoYji0fkIAiCIIjCCilHXobQckSTagRBEAThXkg58jKEDtk0yUYQBEEQ7oWUIy+DfI4IgiAIwrOQcuRl0N5qBEEQBOFZfEY5+uCDD9CiRQuEhIQgIiJCNM3Vq1fRrVs3hISEIDIyEuPHj0dOTg4vzdatW9GgQQMEBgaicuXKWLJkifHCa4EcsAmCIAjCo/iMcpSVlYVnn30Wr776quj53NxcdOvWDVlZWdi9ezeWLl2KJUuW4J133rGnuXTpErp164YnnngCCQkJePPNN/HSSy9h/fr17roNRTKzcwVHyHJEEARBEO7Ez9MCqOXdd98FAElLz4YNG3Dq1Cls3LgRpUuXRr169fD+++9j4sSJmD59OgICArBw4UJUrFgRH3/8MQCgevXq2LlzJ+bNm4dOnTq561ZkyczmW7pORXZHMw/JQhAEQRCFEZ9RjpTYs2cPateujdKlS9uPderUCa+++ipOnjyJ+vXrY8+ePWjfvj3vuk6dOuHNN9+UzDczMxOZmZn232lpaQCA9PR0fW8gj/v37yE9M99adDO3qGFlEQRBFBp6/wisnwp0mQ1Qm1oosfWlrApf3gKjHCUlJfEUIwD230lJSbJp0tPT8ejRIwQHBzvkO3PmTLvViktMTIxeojsQzvvVEJMNK4kgCKKQMdE7ZgkIz3Hv3j2Eh4fLpvGocjRp0iR8+OGHsmlOnz6NatWquUkiRyZPnoyxY8faf1ssFqSkpKBEiRKOK8tcJD09HTExMbh27RrCwsJ0zdsbKOj3BxT8e6T7830K+j0W9PsDCv49GnV/LMvi3r17iI6OVkzrUeVo3LhxGDJkiGyaSpUqqcorKioK+/fv5x1LTk62n7P933aMmyYsLEzUagQAgYGBCAwM5B2TWi2nF2FhYQWywtso6PcHFPx7pPvzfQr6PRb0+wMK/j0acX9KFiMbHlWOSpUqhVKlSumSV/PmzfHBBx/g5s2biIyMBADEx8cjLCwMNWrUsKdZu3Yt77r4+Hg0b95cFxkIgiAIgvB9fGYp/9WrV5GQkICrV68iNzcXCQkJSEhIwP379wEAHTt2RI0aNTBw4EAcPXoU69evx1tvvYVRo0bZLT8jRozAxYsXMWHCBJw5cwYLFizAqlWrMGbMGE/eGkEQBEEQXoTPOGS/8847WLp0qf13/fr1AQBbtmxB27ZtYTabsWbNGrz66qto3rw5ihQpgsGDB+O9996zX1OxYkX8/fffGDNmDD799FOUK1cOixYt8ppl/IGBgZg2bZrDNF5BoaDfH1Dw75Huz/cp6PdY0O8PKPj36A33x7Bq1rQRBEEQBEEUEnxmWo0gCIIgCMIdkHJEEARBEATBgZQjgiAIgiAIDqQcEQRBEARBcCDlyEv44osvUKFCBQQFBaFp06YOAS29lZkzZ6Jx48YoWrQoIiMj0atXL5w9e5aXpm3btmAYhvdvxIgRvDRXr15Ft27dEBISgsjISIwfPx45OfxNeD3F9OnTHeTnRm3PyMjAqFGjUKJECYSGhuLpp592CDbqzfdXoUIFh/tjGAajRo0C4Hvvb/v27XjqqacQHR0NhmGwevVq3nmWZfHOO++gTJkyCA4ORvv27XHu3DlempSUFAwYMABhYWGIiIjAsGHD7GFDbBw7dgyPP/44goKCEBMTg9mzZxt9a3bk7jE7OxsTJ05E7dq1UaRIEURHR2PQoEG4ceMGLw+x9z5r1ixeGk/do9I7HDJkiIPsnTt35qXx5XcIQPSbZBgGc+bMsafx1neopl/Qq93cunUrGjRogMDAQFSuXFlyc3rNsITH+emnn9iAgAB28eLF7MmTJ9nhw4ezERERbHJysqdFU6RTp07sd999x544cYJNSEhgu3btysbGxrL379+3p2nTpg07fPhwNjEx0f4vLS3Nfj4nJ4etVasW2759e/bIkSPs2rVr2ZIlS7KTJ0/2xC05MG3aNLZmzZo8+W/dumU/P2LECDYmJobdtGkTe/DgQbZZs2ZsixYt7Oe9/f5u3rzJu7f4+HgWALtlyxaWZX3v/a1du5adOnUq+9tvv7EA2N9//513ftasWWx4eDi7evVq9ujRo2yPHj3YihUrso8ePbKn6dy5M1u3bl1279697I4dO9jKlSuzzz//vP18WloaW7p0aXbAgAHsiRMn2BUrVrDBwcHsV1995fF7TE1NZdu3b8+uXLmSPXPmDLtnzx62SZMmbMOGDXl5lC9fnn3vvfd475X73XryHpXe4eDBg9nOnTvzZE9JSeGl8eV3yLIs794SExPZxYsXswzDsBcuXLCn8dZ3qKZf0KPdvHjxIhsSEsKOHTuWPXXqFPv555+zZrOZXbduncv3QMqRF9CkSRN21KhR9t+5ublsdHQ0O3PmTA9K5Rw3b95kAbDbtm2zH2vTpg07evRoyWvWrl3LmkwmNikpyX7syy+/ZMPCwtjMzEwjxVXFtGnT2Lp164qeS01NZf39/dmff/7Zfuz06dMsAHbPnj0sy3r//QkZPXo0GxcXx1osFpZlffv9CTsdi8XCRkVFsXPmzLEfS01NZQMDA9kVK1awLMuyp06dYgGwBw4csKf5559/WIZh2OvXr7Msy7ILFixgixUrxru/iRMnslWrVjX4jhwR61iF7N+/nwXAXrlyxX6sfPny7Lx58ySv8ZZ7lFKOevbsKXlNQXyHPXv2ZNu1a8c75ivvUNgv6NVuTpgwga1ZsyavrH79+rGdOnVyWWaaVvMwWVlZOHToENq3b28/ZjKZ0L59e+zZs8eDkjlHWloaAKB48eK848uXL0fJkiVRq1YtTJ48GQ8fPrSf27NnD2rXro3SpUvbj3Xq1Anp6ek4efKkewRX4Ny5c4iOjkalSpUwYMAAXL16FQBw6NAhZGdn895ftWrVEBsba39/vnB/NrKysvDDDz/gxRdf5G2s7Ovvz8alS5eQlJTEe1/h4eFo2rQp731FRESgUaNG9jTt27eHyWTCvn377Glat26NgIAAe5pOnTrh7NmzuHv3rpvuRj1paWlgGMZhX8hZs2ahRIkSqF+/PubMmcObsvD2e9y6dSsiIyNRtWpVvPrqq7hz5479XEF7h8nJyfj7778xbNgwh3O+8A6F/YJe7eaePXt4edjS6NF3+kyE7ILK7du3kZuby6sAAFC6dGmcOXPGQ1I5h8ViwZtvvomWLVuiVq1a9uP9+/dH+fLlER0djWPHjmHixIk4e/YsfvvtNwBAUlKS6P3bznmapk2bYsmSJahatSoSExPx7rvv4vHHH8eJEyeQlJSEgIAAh06ndOnSdtm9/f64rF69GqmpqbwNoX39/XGxySMmL/d92fZntOHn54fixYvz0lSsWNEhD9u5YsWKGSK/M2RkZGDixIl4/vnneZt4vvHGG2jQoAGKFy+O3bt3Y/LkyUhMTMTcuXMBePc9du7cGX369EHFihVx4cIFTJkyBV26dMGePXtgNpsL3DtcunQpihYtij59+vCO+8I7FOsX9Go3pdKkp6fj0aNHkhvKq4GUI0I3Ro0ahRMnTmDnzp284y+//LL979q1a6NMmTJ48sknceHCBcTFxblbTM106dLF/nedOnXQtGlTlC9fHqtWrXLp4/NGvv32W3Tp0gXR0dH2Y77+/goz2dnZ6Nu3L1iWxZdffsk7N3bsWPvfderUQUBAAF555RXMnDnT67eleO655+x/165dG3Xq1EFcXBy2bt2KJ5980oOSGcPixYsxYMAABAUF8Y77wjuU6he8HZpW8zAlS5aE2Wx28NJPTk5GVFSUh6TSzmuvvYY1a9Zgy5YtKFeunGzapk2bAgDOnz8PAIiKihK9f9s5byMiIgKPPfYYzp8/j6ioKGRlZSE1NZWXhvv+fOX+rly5go0bN+Kll16STefL788mj9z3FhUVhZs3b/LO5+TkICUlxafeqU0xunLlCuLj43lWIzGaNm36/+3daUhU3x8G8GfMZpqxXNLJBssfiWJaYKUlUxa0WRJt+KLCaloosgUxWyhop5QoCyKsoIwoEgokaMWl1crKHNtMsjQJjMLSNFus+f5f9J/L3DQN0pyJ5wMX9N5zzpzDmbn3O/eeMwffvn1DZWUlANdoo11QUBD8/PxU78l/oQ8B4Pr16ygrK2vzcwk4Xx/+6rrQXufNX6Xx9PT84y+uDI46mVarRWRkJPLy8pR9NpsNeXl5MJvNnViz3yMiWL58ObKzs5Gfn9/sFm5LrFYrAMBkMgEAzGYzHj58qDqZ2U/m4eHhHVLvP9HQ0IDnz5/DZDIhMjISXbt2VfVfWVkZqqqqlP5zlfZlZmaiV69emDRpUqvpXLn/+vXrh969e6v668OHDygsLFT1V21tLYqKipQ0+fn5sNlsSmBoNptx7do1NDU1KWlycnIQGhrqFI9j7IHRs2fPkJubC19f3zbzWK1WuLm5KY+jnL2Njl69eoWamhrVe9LV+9Du8OHDiIyMRERERJtpnaUP27outNd502w2q8qwp2mXa+cfD+mmP5aVlSU6nU6OHj0qT548kcWLF4u3t7dqlL6zSkxMFC8vL7ly5YpqOmljY6OIiJSXl8vWrVvl3r17UlFRIWfOnJGgoCAZNWqUUoZ9ymZsbKxYrVa5ePGiGI1Gp5nqnpKSIleuXJGKigopKCiQcePGiZ+fn7x580ZEfkxJDQwMlPz8fLl3756YzWYxm81Kfmdvn8iPGZKBgYGydu1a1X5X7L/6+nopLi6W4uJiASDp6elSXFyszNRKS0sTb29vOXPmjDx48ECmTp3a4lT+wYMHS2Fhody4cUNCQkJU08Bra2vF399f5syZI48ePZKsrCwxGAx/bRp4a238+vWrTJkyRfr06SNWq1X1ubTP8rl586bs2bNHrFarPH/+XI4fPy5Go1Hmzp3rFG1srX319fWyatUquXXrllRUVEhubq4MGTJEQkJC5PPnz0oZrtyHdnV1dWIwGCQjI6NZfmfuw7auCyLtc960T+VfvXq1lJaWyv79+zmV/1+zb98+CQwMFK1WK8OGDZPbt293dpV+C4AWt8zMTBERqaqqklGjRknPnj1Fp9NJcHCwrF69WvU7OSIilZWVEhcXJ3q9Xvz8/CQlJUWampo6oUXNzZgxQ0wmk2i1WgkICJAZM2ZIeXm5cvzTp0+ydOlS8fHxEYPBINOnT5fq6mpVGc7cPhGRS5cuCQApKytT7XfF/rt8+XKL70mLxSIiP6bzb9iwQfz9/UWn08nYsWObtbumpkZmzZol3bt3F09PT5k/f77U19er0pSUlEhMTIzodDoJCAiQtLS0v9XEVttYUVHxy8+l/berioqKJDo6Wry8vKRbt24SFhYmO3bsUAUXndnG1trX2NgosbGxYjQapWvXrvLff//JokWLmn2ZdOU+tDt48KDo9Xqpra1tlt+Z+7Ct64JI+503L1++LIMGDRKtVitBQUGq1/gTmv83hIiIiIjAMUdEREREKgyOiIiIiBwwOCIiIiJywOCIiIiIyAGDIyIiIiIHDI6IiIiIHDA4IiIiInLA4IiI/lmVlZXQaDTKkicdYd68eZg2bVqHlU9Efx+DIyJyWvPmzYNGo2m2TZw48bfy9+3bF9XV1Rg4cGAH15SI/iXunV0BIqLWTJw4EZmZmap9Op3ut/J26dLFqVZYJyLXwDtHROTUdDodevfurdrsK4prNBpkZGQgLi4Oer0eQUFBOH36tJL358dq79+/R0JCAoxGI/R6PUJCQlSB18OHDzFmzBjo9Xr4+vpi8eLFaGhoUI5///4dK1euhLe3N3x9fbFmzRr8vAKTzWZDamoq+vXrB71ej4iICFWd2qoDEXU+BkdE5NI2bNiA+Ph4lJSUICEhATNnzkRpaekv0z558gQXLlxAaWkpMjIy4OfnBwD4+PEjJkyYAB8fH9y9exenTp1Cbm4uli9fruTfvXs3jh49iiNHjuDGjRt49+4dsrOzVa+RmpqKY8eO4cCBA3j8+DGSk5Mxe/ZsXL16tc06EJGTaJfla4mIOoDFYpEuXbqIh4eHatu+fbuI/Fj9e8mSJao80dHRkpiYKCKirFBfXFwsIiKTJ0+W+fPnt/hahw4dEh8fH2loaFD2nTt3Ttzc3JQV300mk+zcuVM53tTUJH369JGpU6eKiMjnz5/FYDDIzZs3VWUvXLhQZs2a1WYdiMg5cMwRETm10aNHIyMjQ7WvZ8+eyt9ms1l1zGw2/3J2WmJiIuLj43H//n3ExsZi2rRpGD58OACgtLQUERER8PDwUNKPGDECNpsNZWVl6NatG6qrqxEdHa0cd3d3R1RUlPJorby8HI2NjRg/frzqdb9+/YrBgwe3WQcicg4MjojIqXl4eCA4OLhdyoqLi8PLly9x/vx55OTkYOzYsVi2bBl27drVLuXbxyedO3cOAQEBqmP2QeQdXQci+nMcc0RELu327dvN/g8LC/tleqPRCIvFguPHj2Pv3r04dOgQACAsLAwlJSX4+PGjkragoABubm4IDQ2Fl5cXTCYTCgsLlePfvn1DUVGR8n94eDh0Oh2qqqoQHBys2vr27dtmHYjIOfDOERE5tS9fvuD169eqfe7u7sog5lOnTiEqKgoxMTE4ceIE7ty5g8OHD7dY1saNGxEZGYkBAwbgy5cvOHv2rBJIJSQkYNOmTbBYLNi8eTPevn2LFStWYM6cOfD39wcAJCUlIS0tDSEhIejfvz/S09NRW1urlN+jRw+sWrUKycnJsNlsiImJQV1dHQoKCuDp6QmLxdJqHYjIOTA4IiKndvHiRZhMJtW+0NBQPH36FACwZcsWZGVlYenSpTCZTDh58iTCw8NbLEur1WLdunWorKyEXq/HyJEjkZWVBQAwGAy4dOkSkpKSMHToUBgMBsTHxyM9PV3Jn5KSgurqalgsFri5uWHBggWYPn066urqlDTbtm2D0WhEamoqXrx4AW9vbwwZMgTr169vsw5E5Bw0Ij/9SAcRkYvQaDTIzs7m8h1E1K445oiIiIjIAYMjIiIiIgccc0RELoujAoioI/DOEREREZEDBkdEREREDhgcERERETlgcERERETkgMERERERkQMGR0REREQOGBwREREROWBwREREROSAwRERERGRg/8BZaL1ujskIJwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_6_4()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Due to limited capacity of calculation of my machine, I can't complete this experiment\n",
    "# with 100,000 episodes and 50,000 runs to get the fully averaged performance\n",
    "# However even I only play for 1,000 episodes and 10 runs, the curves looks still good.\n",
    "def figure_6_6():\n",
    "    step_sizes = np.arange(0.1, 1.1, 0.1)\n",
    "    episodes = 1000\n",
    "    runs = 10\n",
    "\n",
    "    ASY_SARSA = 0\n",
    "    ASY_EXPECTED_SARSA = 1\n",
    "    ASY_QLEARNING = 2\n",
    "    INT_SARSA = 3\n",
    "    INT_EXPECTED_SARSA = 4\n",
    "    INT_QLEARNING = 5\n",
    "    methods = range(0, 6)\n",
    "\n",
    "    performace = np.zeros((6, len(step_sizes)))\n",
    "    for run in range(runs):\n",
    "        for ind, step_size in tqdm(list(zip(range(0, len(step_sizes)), step_sizes))):\n",
    "            q_sarsa = np.zeros((WORLD_HEIGHT, WORLD_WIDTH, 4))\n",
    "            q_expected_sarsa = np.copy(q_sarsa)\n",
    "            q_q_learning = np.copy(q_sarsa)\n",
    "            for ep in range(episodes):\n",
    "                sarsa_reward = sarsa(q_sarsa, expected=False, step_size=step_size)\n",
    "                expected_sarsa_reward = sarsa(q_expected_sarsa, expected=True, step_size=step_size)\n",
    "                q_learning_reward = q_learning(q_q_learning, step_size=step_size)\n",
    "                performace[ASY_SARSA, ind] += sarsa_reward\n",
    "                performace[ASY_EXPECTED_SARSA, ind] += expected_sarsa_reward\n",
    "                performace[ASY_QLEARNING, ind] += q_learning_reward\n",
    "\n",
    "                if ep < 100:\n",
    "                    performace[INT_SARSA, ind] += sarsa_reward\n",
    "                    performace[INT_EXPECTED_SARSA, ind] += expected_sarsa_reward\n",
    "                    performace[INT_QLEARNING, ind] += q_learning_reward\n",
    "\n",
    "    performace[:3, :] /= episodes * runs\n",
    "    performace[3:, :] /= 100 * runs\n",
    "    labels = ['Asymptotic Sarsa', 'Asymptotic Expected Sarsa', 'Asymptotic Q-Learning',\n",
    "              'Interim Sarsa', 'Interim Expected Sarsa', 'Interim Q-Learning']\n",
    "\n",
    "    for method, label in zip(methods, labels):\n",
    "        plt.plot(step_sizes, performace[method, :], label=label)\n",
    "    plt.xlabel('alpha')\n",
    "    plt.ylabel('reward per episode')\n",
    "    plt.legend()\n",
    "\n",
    "    # plt.savefig('../images/figure_6_6.png')\n",
    "    # plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:36<00:00,  3.70s/it]\n",
      "100%|██████████| 10/10 [00:36<00:00,  3.65s/it]\n",
      "100%|██████████| 10/10 [00:35<00:00,  3.55s/it]\n",
      "100%|██████████| 10/10 [00:35<00:00,  3.59s/it]\n",
      "100%|██████████| 10/10 [00:34<00:00,  3.47s/it]\n",
      "100%|██████████| 10/10 [00:34<00:00,  3.45s/it]\n",
      "100%|██████████| 10/10 [00:36<00:00,  3.68s/it]\n",
      "100%|██████████| 10/10 [00:34<00:00,  3.47s/it]\n",
      "100%|██████████| 10/10 [00:32<00:00,  3.26s/it]\n",
      "100%|██████████| 10/10 [00:35<00:00,  3.52s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_6_6()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "rl",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
